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KEEGAN WERLIN LLP ATTORNEYS AT LAW

99 HIGH STREET, Suite 2900

BOSTON, MASSACHUSETTS 02110 TELECOP I ER :

——— (617) 951- 1354

(617) 951-1400

October 30, 2019

Mark D. Marini, Secretary Department of Public Utilities One South Station, 5th Floor Boston, MA 02110 Re: Bay State Gas Company d/b/a Columbia Gas of Massachusetts 2019/2020 – 2023/2024 Forecast and Supply Plan Dear Mr. Marini:

On behalf of Bay State Gas Company d/b/a Columbia Gas of Massachusetts (the

“Company”), enclosed is an original and six copies of the Company’s Long-Range Integrated Forecast and System Gas Supply Resource Plan (the “F&SP”), submitted pursuant to G.L. c. 164, § 69I, for the forecast period of November 1, 2019 through October 31, 2024.1

In this filing, the Company presents its forecasting methodology and resource-planning process, along with a strategic resource plan based on the current forecast of customer requirements and market conditions. Approval of the Company’s FS&P is warranted because the plan is in compliance with the demand forecasting and integrated resource planning standards and methods set by the Department of Public Utilities.

Accompanying this letter are Notices of Appearance relating to this docket. Should you have any questions regarding the information provided with this filing, please do not hesitate to contact me directly.

Thank you for your attention to this filing.

Very truly yours,

Steven Frias Encl. cc: George Yiankos, Director, Gas Division Rebecca Tepper, Assistant Attorney General Matthew Saunders, Assistant Attorney General

1 This F&SP replaces the long-range forecast and supply plan previously approved by the Department in Columbia Gas of Massachusetts, D.P.U. 17-166 (2018).

COMMONWEALTH OF MASSACHUSETTS

DEPARTMENT OF PUBLIC UTILITIES Bay State Gas Company ) d/b/a ) D.P.U. 19-XX Columbia Gas of Massachusetts ) _______________________________________)

APPEARANCE OF COUNSEL

In the above-referenced proceeding, I hereby appear for and on behalf of Bay

State Gas Company d/b/a Columbia Gas of Massachusetts.

__________________________ Cheryl M. Kimball, Esq. Keegan Werlin LLP 99 High Street, Suite 2900 Boston, Massachusetts 02110 (617) 951-1400

Dated: October 30, 2019


DEPARTMENT OF PUBLIC UTILITIES Bay State Gas Company ) d/b/a ) D.P.U. 19-XX Columbia Gas of Massachusetts ) _______________________________________)


In the above-referenced proceeding, I hereby appear for and on behalf of Bay

State Gas Company d/b/a Columbia Gas of Massachusetts.

__________________________ Steven Frias, Esq. Keegan Werlin LLP 99 High Street, Suite 2900 Boston, Massachusetts 02110 (617) 951-1400



DEPARTMENT OF PUBLIC UTILITIES _______________________________________________ Bay State Gas Company d/b/a Columbia Gas of Massachusetts 2019 Long-Range Forecast and Supply Plan _______________________________________________

) ) ) ) )

D.P.U. 19-_____


The undersigned counsel hereby appears for and on behalf of Bay State Gas

Company d/b/a Columbia Gas of Massachusetts in the above-referenced proceeding.

Respectfully Submitted, ______________________________ Kenneth W. Christman, Esq. NiSource Corporate Services Company 121 Champion Way, Suite 100 Canonsburg, PA. 15317 Telephone: (724) 416-6315 E-Mail: [email protected]


BAY STATE GAS COMPANY d/b/a COLUMBIA GAS OF MASSACHUSETTS

2019 LONG RANGE FORECAST AND

SUPPLY PLAN 2019/2020 – 2023/2024

Submitted to:

Massachusetts Department of Public Utilities

October 30, 2019

Table of Contents I. INTRODUCTION ......................................................................................................... 1

A. Overview of CMA Services and Resources ..................................................................... 1 B. Standard of Review ............................................................................................................... 2 C. Compliance with Department Directives ......................................................................... 3 D. Organization of the Forecast and Supply Plan ................................................................ 5

II. OVERVIEW OF RESOURCE PLANNING PROCESS ............................................. 8

A. Current Resource Planning Environment ......................................................................... 8 B. Current and Future Market Conditions ........................................................................... 11 C. CMA’s Planning Process ................................................................................................... 24 D. CMA’s Resource Portfolio ................................................................................................ 26

III. FIVE-YEAR DEMAND FORECAST ........................................................................ 31

A. Forecast Methodology and Results .................................................................................. 31

1. Methodology Overview ........................................................................................ 31

2. Summary of Normal Year Forecast Results ..................................................... 32

B. Customer Segment Forecasts ............................................................................................ 34

1. Development ........................................................................................................... 34

2. Variable Descriptions ............................................................................................ 36

3. Customer Segment Model Results ..................................................................... 41

4. Residential Heating Customer Segment............................................................ 41

5. Residential Non-Heating Customer Segment .................................................. 43

6. C&I LLF Customer Segment .............................................................................. 46

7. C&I HLF Customer Segment .............................................................................. 48

8. Capacity Exempt Transportation Demand ....................................................... 50

9. Company Use and Losses .................................................................................... 51

10. Conversion of Billing Period Volumes to Calendar Period Volumes ........ 53

11. CMA’s Energy Efficiency Programs ................................................................. 54

12. Throughput Forecast ............................................................................................. 55

C. Economic Growth Scenarios ............................................................................................. 57 D. Daily Planning Load Forecast Inputs to SENDOUT® ................................................. 57

1. Daily EDD Pattern ................................................................................................. 59

2. Daily Planning Load Shape Model .................................................................... 60

3. Daily Planning Load for SENDOUT® ............................................................. 61

E. Planning Standards and Design Forecasts ...................................................................... 61

1. Introduction ............................................................................................................. 61

2. Weather Data .......................................................................................................... 61

3. Normal Year ........................................................................................................... 61

4. Design Standards and Design Forecast ............................................................. 62

5. Design Year Planning Load Requirements ...................................................... 64

6. Cold Snap ................................................................................................................ 66

IV. RESOURCE PORTFOLIO ANALYSES ................................................................... 68

A. CMA’s Decision-Making Process ................................................................................... 68

1. CMA’s Planning Goals ......................................................................................... 69

2. CMA’s Planning Process ..................................................................................... 69

3. CMA’s Decision-Making Process Employs Least-Cost Planning Techniques .............................................................................................................. 71

4. Analytical Tools ..................................................................................................... 72

B. Description of the Current Resource Portfolio .............................................................. 73

1. Overview of Supply-Side Resources ................................................................. 73

2. Changes to CMA’s Resource Portfolio from the Previous F&SP ............... 75

3. Demand Side/Energy Efficiency Resources .................................................... 77

C. Analyses Utilizing SENDOUT® ...................................................................................... 78 D. Evaluation of Demand-Side Resources .......................................................................... 81 E. Non-Cost Analyses ............................................................................................................. 82 F. Other Information ................................................................................................................ 82 G. Operational Considerations ............................................................................................... 82 H. Springfield Division – Reliability Plan ........................................................................... 84 I. Taunton & Attleboro – AGT Line G Issue .................................................................... 85

V. CMA’S ACTION PLAN .............................................................................................. 86

VI. CONCLUSIONS REGARDING CMA’S RESOURCE PLAN ................................. 88

APPENDICES

Columbia Gas of Massachusetts 2019 Long Range Forecast and Supply Plan

Page 1 of 88

I. INTRODUCTION

The purpose of this report by Bay State Gas Company d/b/a Columbia Gas of

Massachusetts (“CMA” or the “Company”) is to present the long-range forecast and supply plan

(the “F&SP” or the “Plan”) for the period November 1, 2019 through October 31, 2024. The

F&SP details CMA’s resource-planning process and presents the Company’s resource

requirements based on a forecast of customer demand and prevailing market conditions. CMA

submits this F&SP for review and approval by the Department of Public Utilities (the

“Department”) pursuant to G.L. c. 164, § 69I. The Department’s approval of the Company’s F&SP

is warranted because the F&SP sets forth a resource plan to meet expected customer requirements

using the Department’s established forecasting planning processes, standards and methods.

The Company’s FS&P meets the Department’s established standards for approval under

G.L. c. 164, § 69I. The F&SP provides a complete description of the planning processes employed

by the Company, which will enable the Department to adequately review the Plan and to come to

a full understanding of the methods used and the results reached by applying those methods to

current circumstances. The Plan demonstrates that CMA’s planning standards are appropriate and

that the resource strategies described herein are in the best interest of customers and result in a

reliable, long-range, least cost supply to meet the Company’s forecasted firm demand. Lastly, the

Plan demonstrates that the Company’s resource portfolio is sufficient to meet design day, design

winter and design year requirements, as well as demand that could be expected during a cold snap.1

A. OVERVIEW OF CMA SERVICES AND RESOURCES

CMA provides local distribution service to over 320,000 customers residing in three

separate operating divisions, located in areas of Massachusetts surrounding the major cities of

Brockton, Springfield and Lawrence. The majority of CMA’s customer base is comprised of

residential customers. The remainder of CMA’s customers are traditional small and medium-size

commercial and industrial (“C&I”) customers, as well as some larger industrial customers. The

forecast aggregate design day demand for sales customers (“Planning Load”) on CMA’s system

for the upcoming winter is approximately 517 MDth, including the expected demand-side resource

1 The required Energy Facilities Siting Board (“EFSB”) tables are set forth in Appendix 3.


Page 2 of 88

offsets. Normal annual requirements are expected to be about 48.5 MMDth in the initial year of

the Plan.

All of CMA’s customers have the option of purchasing supply from a competitive supplier

and receiving transportation-only service from CMA, pursuant to the Company’s unbundled tariff

options. CMA has over 250 customers being served by five suppliers. The terms and conditions

applicable to transportation-only service specify CMA’s obligation to assign capacity to portions

of the transportation customer loads in each division. CMA’s resource planning process

appropriately reflects its obligation to assign capacity and maintain reliability in conjunction with

its unbundled service offerings.

CMA’s current resource portfolio is comprised of long and short-haul transportation

capacity, storage capacity and associated transportation capacity, city-gate and off-system peaking

supplies and on-system peak-shaving facilities. All of CMA’s upstream long- and short-haul

transportation capacity and underground storage and city-gate peaking supplies are ultimately

delivered to the Company’s divisions located off of the Tennessee Gas Pipeline Company

(“Tennessee” or “TGP”) and Algonquin Gas Transmission, LLC (“Algonquin”) pipelines. CMA’s

on-system peaking facilities include on-system liquid propane gas (“LPG”) and liquefied natural

gas (“LNG”) facilities located within each of its divisions as well as off-system peaking services

that provide deliveries to the Brockton and Lawrence Divisions. The combination of base load,

winter and peaking resources provides a diverse, reliable and cost-effective means of serving

CMA’s overall firm customer and associated demand profile.

B. STANDARD OF REVIEW

The Department assesses each LDC’s long-range planning standards, demand forecasting

methods and resultant design and normal sendout forecasts in order to determine if they are

reviewable, appropriate, and reliable. A forecast method is reviewable, if it “contains enough

information to allow a full understanding of the forecast methodology”; appropriate, if it is

“technically suitable to the size and nature of the particular gas company;” and reliable, if it

“provides a measure of confidence that the gas company’s assumptions, judgments, and data will

forecast what is most likely to occur.” Bay State Gas Company, D.P.U. 08-79, at 2 (2010). The

Department also reviews an LDC’s long-range demand forecasts to ensure that it has accurately

projected gas sendout requirements of the utility’s market area. Lastly, the Department reviews


Page 3 of 88

an LDC’s supply planning process and the resulting resource portfolio with an emphasis on

adequacy and cost. The Department’s review of an LDC’s supply plan investigates whether the

portfolio is adequate to meet forecast firm requirements under design year, design day and cold-

snap conditions for the base case. In instances where the portfolio is not adequate to meet the base

case of forecast requirements, the LDC must demonstrate that it has an adequate Action Plan to

address any deficiency.

C. COMPLIANCE WITH DEPARTMENT DIRECTIVES

The Department’s directives in the Company’s last F&SP proceeding, approved in Columbia

Gas of Massachusetts, D.P.U. 17-166, at 28 (October 30, 2018), have been addressed in this filing.

These directives are below:

(1) Analyze the historical growth pattern shown in the G-Tables against their aggregated

forecast growth pattern and to modify the models to account for unreasonable differences

in the forecasted and historical patterns;

(2) Avoid models that are based on time trend variables and no other causal or logical variables

as such models will not produce realistic and reliable forecasts; and

(3) Avoid using subjectively created variables to capture historical data patterns.

The Company’s 2015 F&SP, reviewed by the Department in Columbia Gas of Massachusetts,

D.P.U. 15-143, at 34 (2016), included the following directive from the Department concerning

contract renewals:

[W]e direct Bay State to ensure compliance with the Department’s protocol for seeking approval of renewals or extensions as a part of all future forecast and supply filings. Bay State must clearly and separately identify those contracts for which it seeks Department approval for renewal or extension, as well as provide the details of all prior Department approvals of each contract for which renewal or extension is being sought, as part of a forecast and supply plan.”

In compliance with this directive, the Company has submitted Table G-24 in this filing.

Table G-24 lists the Company’s pipeline and underground storage contracts and indicates the

contracts for which the Company is seeking approval of renewal in this proceeding. Table G-24


Page 4 of 88

provides detailed information as to each contract, including listing all prior Department approvals

of each contract. In addition, Table G-24 indicates the date for expiration of each contract and the

renewal notice date for each contract. Furthermore, Table G-24 indicates whether each contract

contains an evergreen provision or right-of-first refusal provision.

This F&SP also complies with the Department’s directives issued in the Company’s prior

F&SP proceedings. Bay State Gas Company, D.P.U. 08-79; Bay State Gas Company, D.P.U. 10-

133 (2011), Bay State Gas Company, D.P.U. 11-89 (2012), and Bay State Gas Company, D.P.U.

13-161 (2014). These directives are that the Company should:

(1) Base econometric forecasting models on quarterly data;

(2) Develop individual econometric models for number of customers and use per

customer (“UPC”) or volume for each customer class;

(3) Include sufficiently large data sets in estimating models so that there were enough

degrees of freedom to evaluate the models;

(4) Develop forecasts for residential customer base load by using an econometric model;

(5) Develop econometric forecasts for all customers and not just a fraction of customers

for each customer class;

(6) Include statistically significant causal economic and demographic variables and some

logical dummy variables in the econometric models;

(7) Develop econometric forecasts of transportation load by using causal explanatory

variables representing factors that motivate sales customers to become transportation

customers;

(8) Provide sufficient details and documentation for the Department to evaluate the

Company’s forecasts in its next forecast and supply plan filing;

(9) Include at least ten years of actual data in calculating the Company Use volumes and

the lost and unaccounted for (“LAUF”) percentages for a normal year, calculating

each separately for each division;


Page 5 of 88

(10) Develop separate econometric models of unbilled sales for each division at the

aggregate level;

(11) Carefully review the correlation matrix of driving variables, conduct further analysis

in case of doubt about the presence of multicollinearity, and estimate new models

after resolving the multicollinearity problem if its presence is detected;

(12) Avoid using driving variables wherein the dependent variable accounts for a

significant part of the driving variable;

(13) Where the Company models and forecasts volume directly, develop corresponding

models of number of customers, calculate and analyze the UPC values, and revise the

models as necessary;

(14) Revise models that show consistent over or under forecasting patterns when subjected

to ex-post forecast analysis;

(15) Adjust the results of the econometric model forecast with new customer additions

from the Company’s New Business team;

(16) Describe the process of converting normal-year quarterly forecasts to monthly and

daily planning load;

(17) Calculate design winter EDDs for November through March only; and,

(18) Use Design Day models to develop the design day forecast.

D.P.U. 08-79, at 20-30; D.P.U. 10-133, at 3-5; D.P.U. 11-89, at 28, D.P.U. 13-161, at 14-15.

D. ORGANIZATION OF THE FORECAST AND SUPPLY PLAN

This Plan is organized into six sections, including this Section I (Introduction). Section II

provides a summary of the current resource planning environment, the Company’s resource

planning objectives and goals, and the resource planning process prior to examining each of the

Plan’s elements in more detail. Also, Section II summarizes the Company’s resource planning

tools.

Section III presents CMA’s Demand Forecast, including: (a) an overview of the

methodology that CMA followed to prepare the F&SP demand forecast; (b) a description of the


Page 6 of 88

forecast models that were developed for this F&SP and a summary of the model results;

(c) projected customer demand offsets due to energy efficiency (“EE”) or demand-side resources;

(d) a summary of the derivation of the resource requirements, or “planning load” that CMA used

to assess the adequacy of its resources, including the derivation of several scenarios to reflect

weather-related extremes and optimistic and pessimistic economic scenarios; and (e) a description

for the derivation of the weather-related extremes, or “planning standards” used to derive estimates

of future design day, cold snap and normal and design winter requirements, which are all used in

the Company’s portfolio optimization model.

Throughout Section III, various process improvements implemented by the Company for

this F&SP filing are described. The key changes include: (1) eliminating the pessimistic forecast

scenario, since only the base and optimistic scenarios are utilized for planning purposes;

(2) significantly reducing the number of individual models by combining customer groups, such

as sales and transportation customers, and hence, avoiding the need to separately model the

behavior of transportation customers; (3) reorganizing the C&I customer segment by using load

factor, instead of customer size, to determine customer groupings, which is consistent with other

Massachusetts LDCs; and (4) suspending a separate adjustment for energy efficiency because the

historic billing data includes the impacts of energy efficiency programs.2

Section IV describes the Company’s current resource planning process, including special

considerations given to today’s planning environment and supply-side resource strategies based

on current customer requirements and market conditions. Section V summarizes CMA’s Action

Plan. Lastly, Section VI states CMA’s conclusion regarding its resource plan. The required

Energy Facilities Siting Board (“EFSB”) tables, plus supporting detail for the demand forecast and

the resource assessment, are provided in the appendices to this report. CMA’s Plan incorporates

flexibility and reflects expected future conditions. It is a dynamic living document in the sense

that it continues to be refined as needed in order to reasonably respond to the changing

requirements of CMA’s customers and market conditions. Supply requirements are planned for

and procured within a dynamic environment involving a marketplace influenced by various

2 As explained in Sections II.D and III.B.11, the savings goals in the most recently approved energy efficiency plan

represent a 70 percent increase over the Company’s goals in its prior plan. In this filing, the Company has relied on the historical actual energy savings from its energy efficiency programs to estimate forecasted levels of energy efficiency savings in this F&SP. The Company will review its actual 2019 savings against planned savings and make a supplemental filing in January 2020 based upon its review.


Page 7 of 88

economic conditions. Therefore, the Company’s decisions will be based on current assessments

of the best information known at the time. Final decisions are subject to change. All assessments,

however, will be based upon the methodology set forth in this Plan.


Page 8 of 88

II. OVERVIEW OF RESOURCE PLANNING PROCESS

CMA’s resource planning process begins with the establishment of appropriate goals and

objectives. The primary goal of CMA’s planning process is to acquire and manage resources in a

manner that achieves a least-cost resource portfolio for its customers. A least-cost portfolio

appropriately balances resource cost with CMA’s other planning objectives, which are to maintain

the security and reliability of supply, provide contract flexibility and pursue the acquisition of

viable resources. Pursuit of a least-cost portfolio allows CMA to provide its customers with

reliable service at the lowest possible cost, consistent with the planning criteria required by G.L.

c. 164, § 69I and Department precedent. In addition, CMA’s resource planning process

incorporates the current status of market restructuring in natural gas markets.

A. CURRENT RESOURCE PLANNING ENVIRONMENT

Market and regulatory restructuring of wholesale and retail natural gas markets over the

last few decades have increased the complexity associated with acquiring and managing a least-

cost resource portfolio. Virtually every aspect of LDC portfolio management has been

transformed by regulatory and market changes. In the broadest of terms, the very markets that

LDCs such as CMA participate in, the types of products and services that are bought and sold, and

the manner in which these transactions are completed are vastly different today than they were 40,

30 or even 20 years ago. Market transformation has brought about many new opportunities and

risks for all market participants, including LDCs, which must continue to reliably meet the supply

requirements of their customers.

Natural gas markets continue on a course of broad restructuring that began with the initial

deregulation of most wellhead supply prices starting in 1978 through an act of Congress. Through

a series of physical infrastructure, financial market, regulatory and technological advances, the

manner in which gas supplies are traded and delivered to end-use customers has changed entirely.

Whereas in the past, an LDC or end user might have only been able to procure gas from one or

two entities, today there are many more available choices. The result is a dynamic and more

competitive marketplace that is capable of delivering greater value to customers, but also increases

the complexity of resource planning.

Today, wholesale natural gas commodity markets are no longer price-regulated and the

delivery of supplies to LDC city-gate stations is unbundled from supply and storage services.


Page 9 of 88

Large volumes of gas are traded at many different pooling points along the interstate pipeline

transmission system at transparent prices. LDCs, and even many end users, purchase supplies

directly from marketing entities offering flexible contract terms. Additionally, natural gas

contracts are among the most actively traded futures and options in financial markets. Even

pipeline and storage capacity services are actively traded under more flexible terms in the primary

and secondary release markets.

The restructuring of retail markets has had a significant impact on CMA’s planning process

as customers avail themselves of opportunities to purchase supply from competitive suppliers

pursuant to firm transportation options available under CMA’s tariff. However, the resulting

competitive activity has been uneven in CMA’s various markets. In particular, many large C&I

customers purchase supply from competitive suppliers. While a large number of residential

customers also purchased supply from competitive suppliers during the Company’s Pioneer Valley

Customer Choice Program (“Choice Program”) as well as after issuance of the Department’s

decision in Gas Unbundling, D.T.E. 98-32-B (1998), nearly all of CMA’s residential customers

now take supply service from the Company.3 In addition, over time there has also been turnover

in third-party suppliers serving CMA’s markets.

These changes in natural gas markets have brought greater competition and customer

choice along with increased market instability and uncertainty, substantially complicating the

factors involved and manner in which an LDC forecasts customer demand and designs its resource

portfolio. As the Department recognized in its investigation into the appropriate capacity

assignment methodology, unlike electricity markets, for example, gas markets do not have

centralized bodies such as independent system operators that can effectively take responsibility for

regional reliability. With the introduction of competition from marketers, the LDC remains

responsible for ensuring the supply reliability for its firm sales and non-capacity exempt firm

transportation customers (i.e., “Planning Load”), a responsibility that can be more challenging with

the reliance of marketers to deliver supply to their capacity exempt customers. As the Department

has found in its decision for a mandatory capacity assignment construct, the responsibility of

ensuring supply reliability for the then-existing firm customer base is an appropriate role that CMA

3 The Company understands that the relatively few residential accounts served by third-party suppliers under CMA’s

transportation-only tariff represent primarily multi-family accounts and were acquired primarily as a result of being considered commercial customer business by the suppliers at the time of acquisition. In 2017, a third-party supplier began soliciting and as a result acquired some residential customers.


Page 10 of 88

and other LDCs must fulfill until upstream gas markets are sufficiently robust to be relied upon

for the provision of reliable, low-cost gas deliveries.

Massachusetts gas markets have not yet reached an acceptable level of reliability in

procuring natural gas in the years following D.T.E. 98-32-B, which affects CMA’s planning

strategy. Electric generators are now the largest single consumers of natural gas in New England,

yet the vast majority of these consumers do not sign up for firm pipeline capacity to supply their

needs. Likewise, other large end users typically have shown no propensity to sign up for long-

term pipeline capacity agreements. The net results are regional pipelines that consistently operate

at maximum capacity, winter prices in Massachusetts that are forecasted to be far higher than other

parts of the country and a dramatic reduction in the flexibility historically experienced on the

pipelines that serve CMA. Thus, even though the Department has indicated CMA need not plan

for capacity exempt transportation customers (unless they are expected to return to firm service),

CMA must continue to plan in a manner that ensures adequate and reliable supply so that its

services are not impaired by an upstream disruption, reduction and/or changes to flexibility, or a

failure to deliver natural gas on a critical day. The flow of natural gas on CMA’s system and

reliable delivery through the meters of every customer on its system, regardless of whether that

customer buys its commodity from CMA or someone else, is critical.

Another factor complicating the current planning environment is usage by the Company’s

firm dual-fuel customers. Because these customers are firm, but have the option of using an

alternate fuel, substantial volumes of load can shift to, or from, natural gas depending on relative

fuel prices.

Also, CMA’s customers continue with their strong energy conservation and efficiency

efforts. The Company offers a comprehensive set of energy efficiency (“EE”) programs for

residential, low-income, and commercial and industrial (“C&I”) markets. These programs are

developed as part of the statewide EE effort pursuant to an Act Relative to Green Communities,

Chapter 169 of the Acts of 2008, which was designed to promote enhanced energy efficiency

throughout the Commonwealth through “the acquisition of all available energy efficiency demand

reduction resources that are cost effective or less expensive than supply.” G.L. c. 25, § 21(b)(1).

Stable energy prices and Company-sponsored EE measures wherever cost effective have helped

to increase customer-driven conservation.


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B. CURRENT AND FUTURE MARKET CONDITIONS

The U.S. natural gas industry has experienced significant change over the past fifteen years,

including the discovery and production of natural gas from prolific shale gas deposits. The

increasing domestic production of natural gas has resulted in numerous changes to not only the

broader U.S. natural gas market, but also to the New England region. Specifically, in the New

England region, the increase in domestic natural gas supplies has generally resulted in lower annual

natural gas prices and an increase in the demand for natural gas.

As illustrated in Figure II-1, New England natural gas demand increased by 14 percent

from approximately 803 Bcf (i.e., 2,200 MMcf/day) to 916 Bcf (i.e., 2,508 MMcf/day) between

2009 and 2018. The increase in New England natural gas demand has been driven by power

generation and commercial customer conversions. Specifically, over that time period (i.e., from

2009 to 2018), the natural gas demand by the New England residential, commercial, industrial,

and electric generation segments changed by a compound annual growth rate (“CAGR”) of 0.6,

4.8, 0.8 and 0.6 percent, respectively.

Figure II-1: New England Annual Natural Gas Consumption4

4 Data for certain months in 2018 are based on estimates. Source: U.S. Energy Information Administration, Natural

Gas Consumption by End Use for Massachusetts, Connecticut, Rhode Island, New Hampshire, Vermont and Maine, release date July 31, 2019.

Residential

206 BCF26%

Commercial

140 BCF17%

105 BCF

13%

Electric Generation

351 BCF44%

2009 Total Demand = 803 Bcf

Residential

218 BCF24%

Commercial

214 BCF24%Industrial

113 BCF12%

Electric Generation

370 BCF40%


Industrial


Page 12 of 88

Natural gas demand in New England peaks in the winter-heating season, as shown in

Figure II-2. Over the past decade, there has been at least one winter month where natural gas

demand has reached 100 Bcf, and a peak month demand of 117 Bcf (i.e., 3,776 MMcf/day) was

achieved in January 2019. The January 2019 winter month peak demand of 3,776 MMcf/day is

over 50 percent higher than the 2018 average daily demand of 2,508 MMcf/day.

Figure II-2: New England Monthly Natural Gas Consumption (MMcf)5

The demand for natural gas in New England was approximately 916 Bcf in 2018. Power

Generation made up 40 percent of this usage at 370 Bcf, followed by Residential demand at 218

Bcf, Commercial demand at 214 Bcf, and Industrial demand at 113 Bcf.

In the most recent Annual Energy Outlook (“AEO”), the Energy Information

Administration (“EIA”) is forecasting natural gas usage in New England to be approximately 788

Bcf in 2030. This is made up of Power Generation at 258 Bcf, Residential demand at 204 Bcf,

Commercial demand at 209 Bcf, and Industrial demand at 117 Bcf. Considerable expected

declines in consumption in the Power Generation sector are driving the overall expected decline


Gas Consumption by End Use for Massachusetts, Connecticut, Rhode Island, New Hampshire, Vermont and Maine, release date July 31, 2019.

‐

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

90,000

100,000

110,000

120,000

Jan‐09

Jul‐09

Jan‐10

Jul‐10

Jan‐11

Jul‐11

Jan‐12

Jul‐12

Jan‐13

Jul‐13

Jan‐14

Jul‐14

Jan‐15

Jul‐15

Jan‐16

Jul‐16

Jan‐17

Jul‐17

Jan‐18

Jul‐18

Jan‐19

Residential Commercial Industrial Electric Generation

Jan. 2019


Page 13 of 88

in natural gas usage in New England, while natural gas consumption by the other segments is

expected to remain relatively stable.

Figure II-3: New England Actual and Forecasted Annual Natural Gas Consumption6

In terms of natural gas supply, the New England region historically has had access to

natural gas supplies from four main sources: (1) offshore natural gas supply from Maritime

Canada; (2) imported LNG; (3) natural gas from the Western Canadian Sedimentary Basin and

Dawn Hub; and (4) domestic natural gas (e.g., Gulf Coast and Marcellus/Utica production areas).

However, the natural gas supplies from Maritimes Canada (i.e., Sable Offshore Energy Project

(“SOEP”) and Deep Panuke Offshore Gas Development Project (“Deep Panuke”)) are now

depleted and imported LNG has experienced a significant reduction in overall volumes, and these

changes are impacting the potential reliability of natural gas supply into the New England region.

Natural gas supplies from SOEP and Deep Panuke once represented a significant source of

supply for New England; however, as illustrated in Figure II-4, gas production from both fields

ceased by the end of 2018. From December 1999 through March 2009, the natural gas production


Gas Consumption by End Use for Massachusetts, Connecticut, Rhode Island, New Hampshire, Vermont and Maine, release date July 31, 2019; and U.S. Energy Information Administration, Annual Energy Outlook 2019, Table 2.1: Energy Consumption by Sector and Source – New England, January 24, 2019.

Residential

218 BCF24%

Commercial

214 BCF24%Industrial

113 BCF12%

Electric Generation

370 BCF40%


Residential

204 BCF26%

Commercial

209 BCF26%

Industrial

117 BCF15%

Electric Generation

258 BCF33%



Page 14 of 88

from SOEP averaged approximately 430 MMcf/day; however, operational problems and

production decline at SOEP resulted in final production in December 2018. Starting in the summer

of 2013, natural gas supplies from Deep Panuke augmented the SOEP production; however, due

to operational issues, the Deep Panuke production level was highly variable and much lower than

expected. Average daily Deep Panuke production peaked at approximately 280 MMcf/day in

January 2014, fell to a peak month daily average of 100 MMcf/day in December 2016, and ended

in May 2018. As a result, New England no longer has available natural gas supplies produced

offshore Maritimes Canada.

Figure II-4: Average Daily SOEP and Deep Panuke Production7

In addition to a decrease in natural gas supplies from Maritime Canada, the international

price dynamics for LNG have resulted in a reduction in throughput at the four regional LNG import

facilities. The four regional LNG import facilities include: the Everett LNG terminal in Everett,

Massachusetts, the Canaport LNG terminal in St. John, New Brunswick, and Northeast Gateway

and Neptune LNG, both located offshore Massachusetts. Specifically, as illustrated in Figure II-

7 Source: Canada-Nova Scotia Offshore Petroleum Board, SOEP and Deep Panuke Monthly Production Reports,

accessed July 31, 2019.

0

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MMCF/D

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Page 15 of 88

5, LNG imports at the four regional facilities declined from an average of approximately 670

MMcf/day over the July 2009 to February 2012 time period to approximately 230 MMcf/day since

February 2012. Imports into the Everett LNG terminal declined significantly from an average of

over 400 MMcf/day over the July 2009 to June 2011 time period to an average of less than 130

MMcf/day in the most recent 12 months of data available (i.e., May 2018-April 2019). The imports

at the Canaport LNG facility have also been variable with a decline in peak volume sendout of

over 725 MMcf/day on an average day basis in January 2011 to a peak month average daily volume

sendout of approximately 230 MMcf/day in February 2019. Lastly, there has been infrequent

activity at the two offshore facilities (i.e., Northeast Gateway and Neptune LNG facility), although

Northeast Gateway had some small imports this past winter after being dormant for almost three

years.8

8 In 2013, the Neptune Deepwater Port sought and was granted authorization to suspend LNG import operations for

five years and, according to EIA has not received any LNG since 2010.


Page 16 of 88

Figure II-5: New England LNG Terminals – Average Daily Import Volumes9

Asian LNG prices and the United Kingdom natural gas prices (i.e., the National Balancing

Point (“UK NBP”)) are typically at a premium to the New England markets (as represented by the

Algonquin City-gate (“ALGCG”) price index), except during cold peak winter months when price

spikes in New England markets, which can result in natural gas prices that are higher than

worldwide LNG prices, as illustrated in Figure II-6. Worldwide prices that exceed local New

England contribute to fewer spot cargoes of LNG being delivered to New England as spot LNG

cargoes are drawn to more lucrative markets.

9 Source: U.S. Department of Energy, Office of Fossil Energy, LNG Imports Monthly and Annual Reports, accessed

July 18, 2019; and National Energy Board, Imports of Liquefied Natural Gas, accessed July 18, 2019.

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(MMcf/day)

Everett

Canaport

Northeast Gateway

Neptune


Page 17 of 88

Figure II-6: International Price Dynamics for LNG10

Although there has been a decline in natural gas supplies from offshore Nova Scotia and

imported LNG, there has been a significant increase in the production of natural gas in the adjacent

Appalachia Region (i.e., Marcellus and Utica shale basins). Specifically, as shown in Figure II-7,

since 2009 natural gas production from the Appalachia Region has increased by over sixteen-fold

from an average of less than 2 Bcf/day to over 32 Bcf/day in June 2019.

10 Source: Bloomberg; S&P Global Market Intelligence; Federal Reserve.

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WTI Crude Oil, Average Daily Prices


Page 18 of 88

Figure II-7: Average Daily Shale Gas Production – Appalachia Region11

The increase in natural gas production levels for the Appalachia Region are supported by

an increase in the expected reserves for the Marcellus and Utica shale plays. Specifically, as

illustrated in Figure II-8, the estimated proved reserves for the states of Pennsylvania, West

Virginia, and Ohio in aggregate were almost non-existent prior to 2009; and by 2017, the EIA

increased the estimate to over 150 Tcf. EIA’s 2017 proved reserve estimate for Pennsylvania,

West Virginia, and Ohio now comprises almost half of the shale gas proved reserve estimate for

the entire United States.

11 Source: U.S. Energy Information Administration, Drilling Productivity Report, July 2019.

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Page 19 of 88

Figure II-8: EIA Shale Gas Proved Reserves12

This increase in natural gas reserves estimate for the Marcellus and Utica supply basins

will support sustained production from the shale plays for decades to come. As shown in Figure II-

9, the EIA is forecasting an increase in natural gas production from the East region (i.e., Marcellus

and Utica shale basins) from approximately 27 Bcf/day in 2018 to around 40 Bcf/day by 2030 and

50 Bcf/day by 2050. Therefore, the EIA’s proven reserve estimates and natural gas production

forecasts indicate expected long-term availability of domestic natural gas supply from the

Marcellus and Utica shale basins.

12 Source: U.S. Energy Information Administration, Shale Natural Gas Proved Reserves, release date November 28,

2018.

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Page 20 of 88

Figure II-9: EIA Forecasted Natural Gas Production, East Region13

One of the primary, broader U.S. implications from the growing production of domestic

natural gas is the relatively low and stable price of natural gas in most of the U.S. markets. As

discussed above, the Marcellus and Utica supply basins are becoming the primary domestic natural

gas supply source, and several new pricing points have been added in the Marcellus/Utica

production area (e.g., TGP Zone 4 Marcellus and Millennium East). In addition, natural gas

pricing points in the Marcellus and Utica producing region (e.g., Dominion South Point

(“DOMS”)) are now some of the lowest cost supplies in North America. Figure II-10 illustrates

the historical daily spot prices for DOMS, Henry Hub, and ALGCG.

13 Source: U.S. Energy Information Administration, Annual Energy Outlook 2019, Table 61: Lower 48 Natural Gas

Production and Spot Prices by Supply Region, January 24, 2019.

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Page 21 of 88

Figure II-10: Historical Daily Natural Gas Prices14

As shown in Table II-1, the historical average annual DOMS price index from 2014/15 to

2018/19 was $2.07/MMBtu. In contrast, annual natural gas prices in the New England region (as

represented by ALGCG) averaged $4.16/MMBtu over the same period.

14 Source: NiSource analysis of historical price data from S&P Global Market Intelligence; SNL Financial.

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Page 22 of 88

Table II-1: Natural Gas Price Analysis ($/MMBtu)15

Focusing on the winter period, natural gas prices in the New England region historically

have been significantly higher than DOMS and Henry Hub prices. The average winter price in

New England (i.e., ALGCG) averaged $6.00/MMBtu from 2014/2015 through 2018/2019. In

contrast, the average DOMS winter price over the same time period was $2.34/MMBtu. In other

words, the average winter natural gas price in New England was more than double the winter price

in the adjacent Mid-Atlantic region over the last five years. In addition, the ALGCG winter price

can vary dramatically from year to year. The ALGCG price index had a peak winter average in

the winter of 2013/14 at $16.04/MMBtu and saw its lowest winter average just two years later in

2015/16 at $3.03/MMBtu.

Forward prices indicate that the ALGCG price index will continue to be at a significant

premium to the DOMS price index. Specifically, as shown in Table II-2, the winter average basis

between DOMS and ALGCG was approximately $3.66/MMBtu from 2014/2015 through

2018/2019 and averages approximately $4.30/MMBtu in the futures price for the next three

winters. Stated differently, the average winter ALGCG natural gas price is expected to continue

to be two to three times higher than the DOMS price index.

15 Source: S&P Global Market Intelligence.

Split Year(Nov-Oct) ALGCG DOMS Henry Hub ALGCG DOMS Henry Hub ALGCG DOMS Henry Hub2010/2011 6.59$ 4.34$ 4.09$ 4.61$ 4.26$ 4.15$ 5.44$ 4.29$ 4.13$ 2011/2012 3.88$ 2.82$ 2.76$ 3.30$ 2.71$ 2.69$ 3.54$ 2.75$ 2.72$ 2012/2013 9.61$ 3.48$ 3.47$ 4.29$ 3.58$ 3.77$ 6.42$ 3.54$ 3.65$ 2013/2014 16.04$ 4.32$ 4.67$ 3.64$ 2.83$ 4.21$ 8.64$ 3.43$ 4.40$ 2014/2015 9.39$ 2.33$ 3.24$ 2.58$ 1.36$ 2.70$ 5.36$ 1.76$ 2.92$ 2015/2016 3.03$ 1.22$ 2.00$ 2.64$ 1.32$ 2.57$ 2.80$ 1.28$ 2.34$ 2016/2017 4.54$ 2.60$ 3.03$ 2.67$ 1.89$ 3.00$ 3.44$ 2.18$ 3.01$ 2017/2018 7.62$ 2.45$ 3.02$ 3.28$ 2.40$ 2.95$ 5.03$ 2.42$ 2.98$ 2018/2019 5.42$ 3.10$ 3.37$ 2.36$ 2.15$ 2.55$ 4.16$ 2.71$ 3.03$ Historical Average (2014/15-2018/19) 6.00$ 2.34$ 2.93$ 2.71$ 1.82$ 2.75$ 4.16$ 2.07$ 2.85$ 2019/2020 6.27$ 2.20$ 2.53$ 2.42$ 1.97$ 2.41$ 4.02$ 2.07$ 2.46$ 2020/2021 6.63$ 2.31$ 2.69$ 2.48$ 1.95$ 2.44$ 4.21$ 2.10$ 2.54$ 2021/2022 6.82$ 2.31$ 2.72$ 2.58$ 1.93$ 2.48$ 4.35$ 2.09$ 2.58$ Forward Average (2019/20-2021/22) 6.57$ 2.27$ 2.65$ 2.50$ 1.95$ 2.44$ 4.19$ 2.08$ 2.53$ *2018/19 Historical data through July 31, 2019

Winter (Nov-Mar) Summer (Apr-Oct) Annual (Nov-Oct)


Page 23 of 88

Table II-2: Natural Gas Basis Analysis16

The New England natural gas price levels and high basis differentials are the result of

various natural gas supply and demand dynamics, including the elimination of natural gas supply

from offshore Nova Scotia, as well as capacity constraints on the natural gas pipelines that deliver

supplies from the “South” and “West” (i.e., Algonquin and TGP). Beyond the loss of Maritimes

Canada gas supplies, other primary drivers with respect to the increased utilization of Algonquin

and TGP is the growth in natural gas production from Marcellus Shale and increased use by electric

generators, most of whom do not hold firm capacity commitments. To address the natural gas

supply concerns, price volatility, and infrastructure constraints, New England LDCs are

underpinning several pipeline expansion projects that will increase capacity in New England,

including: Enbridge’s Atlantic Bridge Project, TGP’s Open Season #1101, PNGTS’ Portland

Xpress Project (“PXP”), and Westbrook Xpress Project (“WXP”), as summarized in Table II-3.

16 Source: S&P Global Market Intelligence.

Split Year

(Nov-Oct)ALGCG-DOMS

ALGCG - Henry Hub

DOMS - Henry Hub

ALGCG-DOMS

ALGCG - Henry Hub

DOMS - Henry Hub

ALGCG-DOMS

ALGCG - Henry Hub

DOMS - Henry Hub

2010/2011 2.26$ 2.50$ 0.24$ 0.35$ 0.46$ 0.11$ 1.15$ 1.32$ 0.17$ 2011/2012 1.05$ 1.11$ 0.06$ 0.59$ 0.61$ 0.02$ 0.78$ 0.82$ 0.03$ 2012/2013 6.12$ 6.14$ 0.02$ 0.70$ 0.51$ (0.19)$ 2.87$ 2.77$ (0.11)$ 2013/2014 11.73$ 11.37$ (0.36)$ 0.81$ (0.58)$ (1.38)$ 5.21$ 4.24$ (0.97)$ 2014/2015 7.06$ 6.15$ (0.91)$ 1.22$ (0.11)$ (1.33)$ 3.61$ 2.44$ (1.16)$ 2015/2016 1.81$ 1.04$ (0.77)$ 1.32$ 0.07$ (1.25)$ 1.52$ 0.46$ (1.06)$ 2016/2017 1.95$ 1.52$ (0.43)$ 0.79$ (0.32)$ (1.11)$ 1.26$ 0.43$ (0.83)$ 2017/2018 5.17$ 4.60$ (0.57)$ 0.88$ 0.33$ (0.55)$ 2.61$ 2.05$ (0.56)$ 2018/2019 2.33$ 2.06$ (0.27)$ 0.20$ (0.19)$ (0.40)$ 1.46$ 1.13$ (0.32)$ Historical Average (2014/15-2018/19) 3.66$ 3.07$ (0.59)$ 0.88$ (0.05)$ (0.93)$ 2.09$ 1.30$ (0.79)$ 2019/2020 4.07$ 3.74$ (0.33)$ 0.45$ 0.01$ (0.43)$ 1.96$ 1.57$ (0.39)$ 2020/2021 4.32$ 3.94$ (0.37)$ 0.53$ 0.04$ (0.49)$ 2.11$ 1.67$ (0.44)$ 2021/2022 4.51$ 4.10$ (0.41)$ 0.66$ 0.10$ (0.56)$ 2.26$ 1.77$ (0.50)$ Forward Average (2019/20-2021/22) 4.30$ 3.93$ (0.37)$ 0.55$ 0.05$ (0.49)$ 2.11$ 1.67$ (0.44)$ *2018/19 Historical data through July 31, 2019

Winter (Nov-Mar) Summer (Apr-Oct) Annual (Nov-Oct)


Page 24 of 88

Table II-3: Incremental Pipeline Capacity Projects in New England17

Project Sponsor Pipeline Incremental Capacity

In Service Date(s)

Status

Atlantic Bridge Enbridge (formerly Spectra Energy)

Algonquin/ Maritimes & Northeast

133 Mdth/d (40 MDth/d came online Nov 2017)

Nov 2017 (partial) 2020 (remaining)

Approved by FERC, state permits being appealed

Open Season #1101

Kinder Morgan

TGP 131 MDth/d over three years

50 MDth ‐ Nov 2018 11 MDth – Nov 2019 70 MDth – Nov 2020

2018 & 2019 capacity on time; 2020 capacity has received partial FERC approval

Portland Xpress (“PXP”)*

PNGTS PNGTS Phase 1: 40 MDth/d Phase 2: 0 MDth/d (incremental capacity of 11 MDth/d on joint facilities only) Phase 3: 24 Mdth/d

Nov 2018 Nov 2019 Nov 2020

Full project approved by FERC; Phase 1 in service

Westbrook Xpress (“WXP”)*

PNGTS PNGTS Phase 1: 42 MDth/d Phase 2: 63 MDth/d Phase 3: 18 MDth/d

Nov 2019 Nov 2021 Nov 2022

Phase 1 approved by FERC; Phases 2 and 3 Open Seasons Complete

* Both PXP and WXP are being coordinated with upstream projects on TC Energy (formerly TransCanada) and/or

Enbridge subsidiary Union Gas to transport natural gas from the Dawn Hub and the Western Canadian Sedimentary Basin to northeastern markets.

As a result of these national and regional trends and CMA’s ongoing review of its gas

supply requirements, CMA reviewed various options and capacity paths with the potential to better

satisfy the stated portfolio objectives and has entered into precedent agreements with TGP and

PNGTS under their, respectively, Open Season #1101 and PXP Projects, which were approved by

the Department in D.P.U. 17-172 (May 31, 2018).

C. CMA’S PLANNING PROCESS

This section of the Plan provides an overview of the various elements of CMA’s planning

process, and how each of the elements interact. This planning process has been approved, most

recently in D.P.U. 17-166, by the Department and in other past F&SP proceedings, which more

17 Source: Publicly available documents.


Page 25 of 88

recently have been conducted by the Department every two years. Each element is described in

detail in the following sections of the Plan.

Appendix 1 provides a simplified representation of CMA’s resource planning process. The

process encompasses three major elements: (1) a forecast of requirements; (2) a resource

evaluation; and (3) a resource action plan. Although CMA has employed the same general

planning framework for a number of years, the Company continues to refine its methods and to

update the data relied upon in order to continually improve its planning process.

As more completely described in Sections III.A and III.B, CMA’s planning process begins

with an assessment of customer requirements. CMA employs econometric modeling techniques

to generate its base case forecast of Planning Load. Forecasts are generated separately for four

customer segments: residential heat, residential non-heat, C&I low load factor, and C&I high load

factor, by division, based on models that independently estimate the number of customers and their

associated usage per customer. The development of the forecast models relies on a number of

important data sources including historical customer count, usage, and demographic and economic

variables. In addition to a base case forecast, CMA also prepares an optimistic economic scenario

to establish a range of reasonably expected customer requirements to test the Company’s portfolio

under higher than expected demand. The impact of projected energy efficiency savings is included

in customer forecast requirements as part of the Plan.

The primary design criterion that drives CMA’s customer requirements is weather. CMA

performs statistical analyses of historical weather data to derive planning standards related to

normal year, design winter, cold snap and design day conditions. Resource adequacy is always

measured against design conditions derived from these planning standards.

The second aspect of CMA’s planning process is resource evaluation. CMA’s resource

evaluation encompasses a number of techniques that comprise a thorough process. Resource

evaluation begins with a determination of resource need. Determination of need is accomplished

initially by comparing current daily capacity resources to projected design day customer

requirements, which reflect energy efficiency. Further analysis of need is undertaken by

simulating CMA’s existing portfolio utilizing the SENDOUT® Optimization Model

(“SENDOUT®”) based on its current design winter, design year and cold snap requirements

forecasts. If a need for additional resources is determined, then CMA identifies the potential

resources that are available to meet its customer requirements. These resources may include


Page 26 of 88

renewal or restructuring of existing resources as well as acquisition of additional pipeline, storage,

city-gate-delivered or on-system resources.

Resource evaluation encompasses the assessment of both the cost and non-cost

characteristics of potential available resources. Sophisticated cost analysis is performed utilizing

SENDOUT®, which evaluates the cost impact of changes to CMA’s portfolio by simulating the

daily dispatch of available resources under specified conditions over a defined period of time.

SENDOUT® also possesses the capability to size a least-cost incremental resource or package of

resources based on the total cost impact upon the existing portfolio, including fixed costs. CMA

conducts cost analyses based upon the base and high case forecasts, as well as under design

conditions. Separately, CMA evaluates the non-cost characteristics of alternative resources like

supply security, contract flexibility and supplier viability. Evaluation of the non-cost

characteristics is accomplished through appropriate assessment techniques and scoring.

The Company employs the Total Resource Cost (“TRC”) test, as required and approved by

the Department in its Order in D.P.U. 08-50-A to analyze the cost effectiveness of its gas energy

efficiency programs. The TRC test measures the value of avoided gas supply and any additional

direct economic benefits against the costs of a program to participating customers. The avoided

gas supply costs used in these cost-effectiveness determinations are based on reports prepared for

the avoided energy supply component (“AESC”) study group, as part of the statewide energy

efficiency process.

D. CMA’S RESOURCE PORTFOLIO

An important focus of CMA’s Plan is the effective management of resources in its

portfolio, including the minimization of the associated current and future costs of this portfolio.

During the forecast period, a number of resource decisions must be made primarily related to the

potential renewal or replacement of several individual supply, transportation and storage resources

that currently comprise CMA’s best-cost portfolio. Those decisions, needed to be made within the

first two years of this forecast period, some of which are subject of approval through the

Department’s decision of this Plan, are identified and discussed later in Section IV. Several

upstream pipeline capacity contracts require notice of renewal or termination one year in advance,

and others require an even longer notice. The analysis of renewal or replacement of specific


Page 27 of 88

expiring resources, as well as the acquisition of incremental resources, must take place early in the

planning process in order for CMA to appropriately evaluate all alternatives.

Highlights of CMA’s current resource portfolio are as follows:

Tennessee Transportation and Storage Capacity Contracts: These contracts provide for the delivery of Gulf Coast and/or Appalachian sourced supplies via long-haul transportation capacity, access to market-area storage capacity and short-haul transportation capacity from the United States border and other locations where CMA imports or receives some of its Canadian and U.S. supplies. Since CMA’s Springfield and Lawrence Divisions are served solely by the Tennessee pipeline, it is critical that CMA retain all of its primary delivery-point capacity on Tennessee. The Tennessee capacity are legacy contracts and/or provide a competitively-priced service offering and important supply diversity benefits to the portfolio.

Algonquin Gas Transmission Transportation Capacity Contracts: Supplies transported on Algonquin include production from the U.S. Gulf Coast, Appalachian, and Canadian supply basins, and transportation of storage supplies from TETCO and DTI underground storage facilities. Algonquin is the sole supplier to the Brockton Division customers and CMA must retain primary delivery point capacity to ensure continued service reliability. In addition, the majority of Algonquin contracts are legacy contracts, which represent the most economic transportation option for CMA’s Brockton Division customers.

Iroquois Gas Transmission System, L.P. (“Iroquois”) Transportation Capacity Contract: CMA has one contract on Iroquois for transportation of underground storage volumes and pipeline supplies from Dawn, Ontario onto Tennessee for ultimate delivery to all of CMA’s service areas.

National Fuel Gas Supply Corporation (“National Fuel”) Transportation and Storage Capacity Contracts: CMA has storage and transportation legacy contracts with National Fuel that provide for the delivery of underground storage supplies into Tennessee for transport to the Company’s Springfield and Lawrence Divisions. These legacy contracts provide much needed balancing flexibility and supply reliability.

Dominion Transmission Inc. (“DTI”) Storage Capacity Contracts: CMA has a legacy storage with DTI that provides for the delivery of underground storage supplies to Texas Eastern Transmission, LP (“TETCO”) for transport to the Company’s Brockton Division. This contract provides much needed balancing flexibility and supply reliability for customers.


Page 28 of 88

Union Gas Transportation Contract: The Company has several firm transportation contracts on Union, which provides access to supply and storage at Dawn Hub.

Transcontinental Gas Pipe Line (“Transco”) Transportation Capacity Contract: CMA has one legacy contract on Transco that transports storage volumes onto Algonquin for ultimate delivery to CMA’s Brockton Division.

Texas Eastern (“TETCO”) Transportation and Storage Capacity Contracts: CMA has TETCO long-haul firm transportation capacity contracts that provide United States Gulf Coast supplies to its Brockton Division customers. CMA also holds market area storage on the TETCO system and associated short-haul transportation capacity to the Brockton Division. Further, CMA holds short-haul TETCO transportation capacity from DTI storage to the Brockton Division. These legacy contracts provide much needed balancing flexibility and supply reliability.

Enbridge Storage: The Company has two storage contracts with Enbridge Storage. The storage services are located at Dawn Hub and provide a natural hedge against winter prices increases. The supply from these storage services can be delivered to CMA’s Brockton, Springfield and Lawrence Divisions.

Portland Natural Gas (“PNGTS”) Transportation Contracts: The Company has several firm transportation agreements with PNGTS. These contracts allow for the delivery of supply and storage from the Dawn Hub primarily to the Company’s Springfield and Lawrence Divisions.

Repsol Energy North America Corporation (“Repsol”): The Company has two contracts for firm seasonal deliveries of re-gasified LNG from Repsol’s Canaport LNG terminal. These supplies are designed for delivery to TGP capacity at Dracut, MA.

Millennium Pipeline Company (“MPC”): The Company has one firm transportation agreement with MPC allowing for delivery of supply into AGT for delivery to the Company’s Brockton Division.

Granite: The Company has one firm transportation agreement that allows for delivery to a Northern Utilities city-gate as part of an Exchange Agreement. CMA receives gas at Lawrence, Springfield and Brockton from Northern Utilities as reciprocation.

Energy Efficiency (“EE”) Plan Filing: CMA’s Three-Year EE Plan encompassing a portfolio of EE programs and measures planned for the three years ending December 31, 2021, was approved by the Department in D.P.U. 18-110 (January 29, 2019). The savings goals in the 2019-2021 EE Plan represent a 70% increase over the Company’s goals in its 2016-2018 EE Plan. Specifically, the 2019 savings goal is a 61% increase over Year 1 of the 2016-2018 EE Plan


Page 29 of 88

(i.e., 2016) goal; the 2020 savings goal is a 75% increase over Year 2 of the 2016-2018 EE Plan (i.e., 2017) goal; and the 2021 savings goal is a 74% increase over Year 3 of the 2016-2018 EE Plan (i.e., 2018) goal. The 2019-2021 savings goals are also an average of 53% higher than the record actual savings achieved by CMA in 2018. Given the magnitude of this increase over historical achievement and prior savings goals, CMA has relied on the historical actual energy savings from EE programs to estimate forecasted levels of EE savings in this F&SP.18 The Company will review its full year 2019 achieved savings against 2019 savings goals and make a supplemental filing in January 2020 based upon its review.19 The Company will also continuously monitor the cost effectiveness of its EE Plan.

An important consideration in determining whether renewal of legacy contracts is

consistent with a least-cost strategy is the cost of new capacity. During the past five to ten years,

most new pipeline projects built in the region have charged marginal-cost-based rates for the

associated incremental pipeline capacity. Marginal-cost-based rates are higher than current legacy

capacity20 rates on the pipelines that serve CMA. These legacy pipeline rates and associated

capacity are advantaged by lower initial construction costs and significant depreciation of their

plant and rate base, of which the revenue requirement is recovered by pipelines at average cost-

based rates. These lower rates result in higher load factors and higher billing determinants, which

in combination help to further maintain the lower rates associated with these legacy pipeline

contracts. Further, because legacy transportation capacity is fully subscribed from the reliable,

low-cost basins to the south and west, the only opportunity for the Company to replace these

needed resources would be (a) from higher cost resources sourced from the north and east;21 or

(b) in the event that new capacity is constructed providing the same, firm, primary delivery

capacity that the Company has available through these legacy contracts.

18 No other LDC experienced an increase to this degree. The next highest savings goals increase for an LDC is 13%

from the 2016-2018 EE Plan to the 2019-2021 EE Plan.

19 Through September 2019, the Company has achieved 35 percent of 2019 savings goals.

20 Legacy capacity is defined here as firm interstate pipeline transportation and storage service provided to CMA and other New England LDCs under FERC-approved rate schedules, which were in effect upon or soon after the unbundling of the United States’ interstate pipeline system resulting from FERC Order No. 636.

21 As noted hereinabove, the Company has executed agreements for capacity from TGP/PNGTS/TCPL/Union and Repsol. While these resources are generally more costly than the Company’s traditional legacy contracts, the Company has exerted much effort to ensure access to highly reliable supplies as compared to the increasing uncertainty of supplies procured at New England supply points, for example, Dracut, MA.


Page 30 of 88

In the context of this report, CMA has therefore reflected the renewal and continuation of

all legacy capacity resources, for which the Company has a right-of-first refusal or a rollover right

that comes up for renewal during the five-year planning horizon of the Plan. Legacy contracts are

typically renewed because these long-term pipeline contracts provide competitively-priced

services, offer important supply diversity benefits to the Company’s best-cost portfolio and are

needed to fulfill CMA’s firm-service obligations.

In addition, rollover of the Company’s other existing capacity with a right-of-first refusal

or roll-over right has historically proven to be far more economical than procuring capacity on

most of the new pipeline projects available to the Company. This valuable legacy capacity is

expected to continue to be more economical in the future, and therefore, the Plan reflects these

rollovers. The Company notes, however, that when making renewal, replacement, or incremental

capacity decisions, it will employ the planning, supply and capacity acquisition methods approved

under this Plan to further ensure that the decision-making process used is reasonable and

appropriate, and that the decision is based on the best information available to CMA at the time it

is made.

CMA’s on-going evaluation of these resource strategies will be reflected in its Resource

Action Plan in Section V (“Action Plan”). The Action Plan includes the results of CMA’s resource

assessments and the factors that CMA will evaluate in making its decisions to contract or de-

contract for capacity in order to satisfy its obligation to meet firm customer demand and, in the

process, ensure that each decision constitutes the best available alternative at the time it is made.

All new supply and capacity contracts entered into by the Company for more than one year will

be filed with the Department for approval, as required by law and Department precedent. The

Company has identified in Table G-24, page 1, those contracts that expire within two years of this

F&SP filing date.


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III. FIVE-YEAR DEMAND FORECAST

A. FORECAST METHODOLOGY AND RESULTS

1. Methodology Overview

The primary objective of the demand forecast process is to determine the Company’s

forecast of “planning load”22 under normal and design weather conditions, and base and optimistic

economic scenarios in order to assess the adequacy of the Company’s resource plans relative to

the extreme conditions that are represented by these weather and economic scenarios. The

planning load requirements forecast is derived from forecast models that were developed for four

customer segments23 for the gas-supply planning years 2019/20 through 2023/24 (the “forecast

period”).24 The weather-related scenarios that are applied to the forecast models include normal

year, design year, design day, and cold snap standards. CMA uses an optimistic economic scenario

forecast as an alternative to the base case forecast.

Separate demand forecasts were developed for CMA’s three divisions, Brockton,

Lawrence and Springfield, using the same process for each division. Quarterly base case forecasts

for each customer segment were estimated by the Company by applying standard econometric

techniques to data representing normal weather conditions and base case economic and

demographic conditions. The economic and demographic variables in the forecast models were

identified in the modeling process to be the major factors influencing natural gas demand in each

of the Company’s service territories. The Company’s planning load forecast was determined by

starting with customer segment demand forecasts and (a) subtracting forecasted capacity exempt

demand; and (b) adjusting for Company Use, losses and unbilled volumes.

This Section of the F&SP Report includes a description of the demand forecast

methodology, models, and results.

This report uses the definitions that are listed below in Table III-1 to refer to and distinguish

between different types of natural gas demand.

22 Planning load is the load for which the Company must procure resources (see definition at Table III-1: Forecast Terms).

23 The customer segments are: (1) Residential Heating; (2) Residential Non-Heating; (3) Commercial and Industrial Low-Load Factor(“C&I LLF”); and (4) Commercial and Industrial High-Load Factor (“C&I HLF”). In addition, the Company developed forecasts for Company Use, Lost and Unaccounted for gas volumes (“LAUF”) and unbilled volumes.

24 A gas supply planning year or “Gas Year” consists of the twelve months November through October. A “Split Year” denotes the twelve months from October through September (i.e., Q4-Q3).


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Table III-1: Forecast Terms

Term Definition

Demand, Usage, Volume or Load

Generic terms that refer to the gas used by customers to meet their energy requirements.

Customer Segment Demand

Total firm sales plus total firm transportation demand (measured at the customer meter on a billing period basis) for a customer group, which is a defined group of rate classes.

Throughput Total usage measured at the gate station on a calendar period basis; throughput equals the sum of (a) sales plus total transportation gas use measured at customers’ meter, (b) Company Use, (c) lost and unaccounted for gas and (d) unbilled volumes.

Capacity Exempt customers

Transportation customers that are not subject to the capacity assignment provisions as set forth in the Company’s Distribution And Default Service Terms and Conditions, Section 13, M.D.P.U. No. 142.

Non-Capacity Exempt (or Capacity Assigned) customers

Transportation customers that are subject to the capacity assignment provisions as set forth in the Company’s Distribution And Default Service Terms and Conditions, Section 13, M.D.P.U. No. 142.

Planning Load Total firm sales plus non-capacity exempt transportation usage measured at the gate station on a calendar period basis (i.e., includes Company Use, and losses) – excludes capacity exempt transportation load.

2. Summary of Normal Year Forecast Results

As determined in the forecast process that is described in Section III.B, CMA’s normal

year planning load,25 including the effects of expected future energy efficiency measures, is

projected to increase at a 0.83 percent compound annual growth rate (“CAGR”) over the forecast

period. Residential demand is forecasted to increase at a 1.14 percent annual rate,26 and C&I

demand is forecasted to increase by 0.31 percent per year during the forecast period.27 The

planning load forecast results are summarized in Table III-2 below.

25 Includes all firm sales and firm transportation customer demand, Company Use, Lost and Unaccounted for Sales;

excludes all interruptible, and capacity exempt demand.

26 Includes Residential Heating and Residential Non-Heating.

27 Includes C&I LLF and C&I HLF.


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Table III-2: Normal Year Planning Load Forecast Results Summary (Dth)28

Gas YearResidential

Demand C&I Demand Company Use

Firm Unbilled and

Losses

Total Firm After

Unbilled and Losses

Capacity Exempt

Demand After Unbilled and

Losses

Total Planning

Load

2019-2020 14,008,718 12,056,082 24,414 439,582 26,528,797 1,747,608 24,781,189 2020-2021 14,240,508 12,121,897 24,414 444,597 26,831,417 1,745,220 25,086,197 2021-2022 14,445,584 12,172,631 24,414 448,907 27,091,536 1,744,135 25,347,401 2022-2023 14,646,746 12,119,553 24,414 451,402 27,242,116 1,743,521 25,498,595 2023-2024 14,844,751 12,080,849 24,414 454,086 27,404,100 1,743,294 25,660,807 CAGR 1.46% 0.05% 0.00% 0.81% 0.81% -0.06% 0.88%

Brockton

Gas YearResidential


Firm Unbilled and

Losses

Total Firm After

Unbilled and Losses

Capacity Exempt


Losses

Total Planning

Load

2019-2020 8,585,253 10,549,556 32,346 322,951 19,490,106 3,850,058 15,640,048 2020-2021 8,624,003 10,558,291 32,346 323,751 19,538,392 3,849,668 15,688,724 2021-2022 8,670,233 10,641,918 32,346 325,939 19,670,435 3,850,094 15,820,341 2022-2023 8,789,976 10,667,297 32,346 328,384 19,818,003 3,850,345 15,967,659 2023-2024 8,868,994 10,718,090 32,346 330,572 19,950,001 3,850,590 16,099,411 CAGR 0.82% 0.40% 0.00% 0.58% 0.58% 0.00% 0.73%

Springfield

Gas YearResidential


Firm Unbilled and

Losses

Total Firm After

Unbilled and Losses

Capacity Exempt


Losses

Total Planning

Load

2019-2020 4,691,222 3,838,083 32,536 144,260 8,706,101 935,844 7,770,257 2020-2021 4,727,981 3,914,756 32,536 146,171 8,821,445 935,961 7,885,484 2021-2022 4,759,862 3,950,623 32,536 147,313 8,890,333 936,109 7,954,224 2022-2023 4,795,471 3,965,903 32,536 148,170 8,942,081 936,209 8,005,872 2023-2024 4,832,913 3,973,493 32,536 148,929 8,987,871 936,270 8,051,601 CAGR 0.75% 0.87% 0.00% 0.80% 0.80% 0.01% 0.89%

Lawrence

Gas YearResidential


Firm Unbilled and

Losses

Total Firm After

Unbilled and Losses

Capacity Exempt


Losses

Total Planning

Load

2019-2020 27,285,193 26,443,721 89,296 906,793 54,725,004 6,533,511 48,191,494 2020-2021 27,592,493 26,594,945 89,296 914,519 55,191,254 6,530,849 48,660,405 2021-2022 27,875,678 26,765,171 89,296 922,159 55,652,304 6,530,338 49,121,966 2022-2023 28,232,194 26,752,753 89,296 927,956 56,002,200 6,530,074 49,472,126 2023-2024 28,546,657 26,772,432 89,296 933,586 56,341,972 6,530,154 49,811,818 CAGR 1.14% 0.31% 0.00% 0.73% 0.73% -0.01% 0.83%

Total


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B. CUSTOMER SEGMENT FORECASTS

1. Development

The customer segment forecast was developed by preparing separate quarterly forecast

models for the following four customer segments for the Company’s three operating divisions,

Brockton, Lawrence, and Springfield:

Residential Heating

Residential Non-Heating

Commercial and Industrial Low-Load Factor (“C&I LLF”)

Commercial and Industrial High-Load Factor (“C&I HLF”)

CMA developed separate econometric models for the number of customers and use per

customer for the residential and C&I customer segments. The Company made two major structural

changes in order to improve and simplify the Company’s customer segment forecasts.

First, sales and transportation customers were modeled together for this filing as opposed

to separately modeling sales and transportation as was done in the past. The Company is required

to procure capacity for planning load customers, which includes capacity assigned transportation

customers. Historically, the Company has modeled sales separately from transportation

customers, added the sales and transportation forecasts to create a total, then subtracted capacity

exempt from that total. In this filing the Company has modeled sales and transportation customers

together, and then subtracted capacity exempt. This change increases efficiency, as it significantly

decreases the number of models the Company must build, and reduces the challenges associated

with modeling transportation customer behavior.

Second, the Company reorganized the C&I customer segments to be more consistent with

the customer segments used by other Massachusetts LDCs. In previous filings, the Company

modeled C&I in two categories based on size of customer: (1) C&I was comprised of G/T-40, 41,

42, 50, 51, and 52 customers, and (2) C&I extra-large volume (“XLV”) was comprised of G/T-43

and 53 customers. In previous filings, the C&I XLV customer segment often represented a small

number of very large customers (approximately 50 or fewer), which can be difficult to accurately

model. In this filing, the Company has modeled the C&I customer segments based on load factor,

(i.e., LLF and HLF), which is consistent with other Massachusetts LDCs and produces customer

segments that have at least 600 customers.


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CMA also provides transportation service to several customers according to the terms of

negotiated Special Contracts. CMA has not developed demand forecasts for these customers

because these Special Contract customers are capacity exempt and therefore not part of the

planning load.

All regression analyses were conducted using the SAS software package. Regression

modeling techniques were used to develop the number of customers, use per customer and total

demand forecasts based on independent (explanatory) variables such as weather, natural gas prices,

and other economic and demographic variables. Each model was tested and corrected for

autocorrelation, heteroskedasticity, instability, multicollinearity, ex post and outlier issues. The

modeling development process and specific statistical techniques are discussed in Appendix 4 and

Appendix 8.

CMA projected its customers’ requirements for each division based on forecast models

derived by the Company and forecasts of future economic conditions in the region. The total

demand for residential and C&I customer segments for each division and each quarter in the

forecast period is calculated by multiplying the forecasted number of customers by the forecasted

use per customer in that forecasted quarter. For each division the Company also derived models

for capacity exempt demand. The capacity exempt demand forecasts were subtracted from total

firm customer segment demand to produce planning load so that CMA’s resource planning

properly accounts for the third-party supplies serving this segment of CMA’s load. Projected

customer requirements are forecast for the base case as well as for the optimistic economic scenario

to ensure that CMA’s portfolio is adequate to meet its customer requirements under a complete

range of potential future conditions.

Unbilled models were developed to convert billing period data to calendar period.

Company Use and Lost and Unaccounted For (“LAUF”) forecasts were developed and added to

the division-specific forecasts to produce a full planning load forecast for resource planning

purposes.

CMA also performs a statistical analysis of historical weather data for each division to

determine its design planning standards. The design planning standards establish the design day,

design winter and cold snap conditions that CMA’s resource portfolio must satisfy in order to

ensure system safety, integrity and reliability. CMA’s models that include weather variables

were re-run with design weather as inputs to produce forecasted demand under design weather


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conditions.

2. Variable Descriptions

The first step in the demand forecasting process is the collection of various historical and

projected data required to develop the forecast models. The forecasting models used to derive

CMA’s five-year demand forecast rely on a number of internal and external data sources.

Historical values of the dependent variables in these forecast models are obtained from CMA

billing data for customer counts and delivered volumes.29 Use per customer values were calculated

using the customer and volume billing data. Independent variables for the forecast models include

measures for weather, demographics, and economics. Historical and projected future conditions

for the economic independent variables are obtained from IHS Global Insight and Itron. The

general data and variable categories that were utilized in the development of the forecast are

described below.

a. Customer Segment Data

The Company analyzed monthly billing data by customer class for the Brockton and

Springfield divisions for historical periods ending in December 2018. The Company analyzed

monthly billing data by customer class for Lawrence for the historical period ending June 2018

(i.e., the end of the last full calendar quarter) to omit data impacted by the Merrimack Valley

incident. The beginning points for the models vary depending on the earliest date that data were

available for the particular combination of variables in the model for the customer class, division

and model statistics. The customer class data were compiled into the four CMA customer

segments, as shown in Table III-3 below.

29 As used in this report, the term “volume” refers to therms or multiples of therms.


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Table III-3: Customer Segment Definitions

Rate Class Customer Segment

R-1, R-2, T-R1, T-R2

Residential Non-Heating Sales and Transportation Service

R-3, R-4, T-R3, T-R4

Residential Heating Sales and Transportation Service

G-40, G-41, G-42 T-40, T-41, T-42

G-43, T-43,

Low-Load Factor C&I Sales and Transportation Service

“C&I LLF”

G-50, G-51, G-52 T-50, T-51, T-52,

G-53, T-53

High-Load Factor C&I Sales and Transportation Service “C&I HLF”

The following is a summary of the process that was used to compile quarterly Customer

Segment data:

Company billing month customer, usage, and revenue data for each rate class were

collected for the historical period beginning as early as January 1991 and as late as

January 2002, depending on customer segment, through December 2018.30

The rate class data were compiled into Customer Segments as defined by the table

above; and

The data were aggregated into billing quarters to be used in the customer, use per

customer and total volume quarterly forecast models.31

b. Weather Variable

Effective Degree Days (“EDDs”), available on a billing cycle basis beginning in 1993

(billing cycle EDD data before 1993 are not available), were utilized as the weather measure; daily

NOAA weather data were purchased for each of the Company’s three divisions from a weather

30 The years used in each model depended on the availability of independent variables.

31 Quarterly models were developed based on standard calendar quarters (Q1 = January, February, March; Q2 = April, May, June; Q3 = July, August, September; Q4 = October, November, December).


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consulting firm, and used by the Company to calculate EDDs. EDDs are Heating Degree Days

(“HDD”) that have been adjusted for average daily wind speed.32

Since the Customer Segment load data are measured in billing quarters,33 the historical

daily EDD data were converted to a billing quarter basis. The billing quarter EDD calculations

are described in Appendix 6.

c. Natural Gas Price Variable

Because economic theory suggests that price is likely to influence demand, a natural gas

price variable was developed to be included in the customer segment models. Data to construct

independent variables for the price of gas were obtained from Company resources; historical data

were obtained from Company billing records and forecast data were created using inputs from the

Energy Information Administration (“EIA”). The price variables are adjusted for inflation using

deflators provided by IHS Global Insight. The development of the price variable is explained in

detail in Appendix 11.

d. Economic and Demographic Variables

Economic theory suggests that other economic and demographic variables may also affect

demand. To reflect economic and demographic data for each of CMA’s operating divisions, the

Company obtained energy efficiency data from Itron and obtained combined county data, as

described in Table III-4, for the historical periods ending in 2018, from IHS Global Insight. The

historical period start date varies with economic concept.

The Brockton Division includes the following counties: Bristol, Norfolk and Plymouth.

The Lawrence Division includes Essex County.

The Springfield Division includes the following counties: Hampden and Hampshire.

32 A heating degree day is a unit of measure for recording how cold it has been over a 24-hour period. The number of

degree days applied to any particular day of the week is determined by calculating the mean temperature for the day and then comparing the mean temperature to a base value of 65 degrees Fahrenheit. For example, if the mean temperature for the day is 55 degrees, then there have been 10 heating degree days (65 minus 55 equals 10).

33 CMA and most gas distribution companies record customer, usage, and revenue data that reflects the staggered schedule CMA follows to read approximately 5 percent of its customers’ meters each business day of a month. In general, according to this staggered schedule, “billing cycle” 1 meters are read on one of the first days of a calendar month and meters for the last billing cycle, generally billing cycle 20 or 21, are read on one of the last days of the calendar month. Thus the recorded gas demand for all billing cycles in a billing month was actually used at some time during that calendar month or the prior calendar month, in general.


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Table III-4: IHS Global Insight Data

Economic and Demographic Data

Employment (NAICS), Total Nonfarm (Thous.) Employment (NAICS), Non-Manufacturing Employment (NAICS), Service Providing Private (Thous.) Employment (NAICS), Construction, Natural Resources, and Mining

(Thous.) Employment (NAICS), Manufacturing (Thous.) Employment (NAICS), Transp., Trade, & Utilities (Thous.) Employment (NAICS), Information (Thous.) Employment (NAICS), Financial Activities (Thous.) Employment (NAICS), Professional & Business Svcs (Thous.) Employment (NAICS), Educational & Health Svcs (Thous.) Employment (NAICS), Leisure & Hospitality (Thous.) Employment (NAICS), Other Services (Thous.) Employment (NAICS), Government (Thous.) Employment (NAICS), Federal Government (Thous.) Employment (NAICS), State & Local Government (Thous.) Employment (NAICS), Military (Thous.) Personal Income (Millions) Wage Disbursements, Total Nonfarm (Millions) Nonwage Income (Millions) Average Annual Wage, NonFarm Employment (Thous.) Per Capita Personal Income (Thous.) Average Household Income (Thous.) Real Personal Income (Millions 2009$) Real Wage Disbursements, Total Nonfarm (Millions 2009$) Real Nonwage Income (Millions 2009$) Real Per Capita Personal Income (Thous. 2009$) Gross County Product (Millions) Real Gross County Product (Millions 2009$) Population (Thous.) Population, Age 0 thru 14 (Thous.) Population, Age 15 thru 24 (Thous.) Population, Age 25 thru 34 (Thous.) Population, Age 35 thru 44 (Thous.) Population, Age 45 thru 54 (Thous.) Population, Age 55 thru 64(Thous.) Population, Age 65 and Older (Thous.) Population, Age 0 thru 24 (Thous.) Heads of Households (Thous.) Heads of Households, 24 and Under (Thous.)


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Economic and Demographic Data

Heads of Households, Age 25 thru 34 (Thous.) Heads of Households, Age 35 thru 44 (Thous.) Heads of Households, Age 45 thru 54 (Thous.) Heads of Households, Age 55 thru 64 (Thous.) Heads of Households, Age 65 and Older (Thous.)

Prices

Henry Hub cash market price of natural gas, dollars per million Btu, IHS Global Insight

GDP Price Deflator

Consumer Price Index, All Urban, 1982-84 = 1.00, BLS GDP Implicit Price Deflator, 2000 = 1.00, BEA

e. Other Variables

The following additional variables were created to be used in the development of the

customer segment models:

Trend variables to represent changes in the number of customers or use per

customer that were a function of time.

Binary variables (or dummy variables) to represent time-related events.34

Interaction terms related to the dummy variables to represent changes in the

relationships between the dependent variable and independent variables as a result

of time-related events.

Lagged billing EDD deviation from normal billing EDD (Q4 to Q3) by division to

capture the reclassification of customers from C&I HLF to C&I LLF following a

colder-than-normal winter and vice versa.

Log transformations of use per customer variables and associated independent variables,

including price, were evaluated to produce elasticities that are more transparent in evaluating

model estimates, where the assumption of constant elasticity seemed reasonable and to alleviate

potential problems related to heteroskedasticity.

34 These time-related dummy variables equal 1 when that specific time-related event occurs, and equal 0 outside of that

specific time.


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3. Customer Segment Model Results

The following sections summarize each customer segment model and the model forecast

results. CMA tested each model for the presence of autocorrelation, heteroskedasticity,

multicollinearity, stability, and outliers, and made corrections if the tests indicated the need to do

so. The Company also performed an ex post analysis for each model and modified the model

specification if necessary. Detailed statistical results for each customer segment model are

contained in Appendix 9. In addition to the model statistics and the results of the tests for

autocorrelation, heteroskedasticity, multicollinearity, stability, and outliers, Appendix 9 includes

notations to explain the dummy variables that were included in each model. Also, graphs of the

dependent variables for each customer segment are provided in Appendix 5. As explained in

Appendix 8, Chow tests were used to identify the presence of structural shifts for each model. The

graphs of dependent variables in Appendix 5 provide additional support for some of the structural

shifts.

4. Residential Heating Customer Segment

Residential Heating is the largest CMA customer segment in terms of number of customers

and demand across all three divisions. From 2014 through 2018, the Residential Heating customer

segment represented approximately 84 percent of total customers and approximately 50 percent of

total actual demand. Over the same time period, the number of Residential Heating customers

increased by 1.51 percent per year.

a. Residential Heating Customer Model Results

Economic theory suggests that the number of Residential Heating customers may be

dependent on such variables as the number of people living in the service territory (i.e., households

or population), and income levels (i.e., personal income or income per capita). In addition, during

a four quarter cycle, CMA serves the most customers in the coldest months, i.e. Quarter 1, and the

fewest customers in the warmest months, Quarter 3; quarterly dummy variables are included in

these models to reflect this seasonal pattern in the number of CMA residential heating customers.

The general form of the selected Residential Heating customer models is:

Total customer count = (a) + (b)*Population + (ci ) Quarterly Dummiesi+ (di ) Binary Variablesi


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Local population is a primary explanatory variable in the Residential Heating customer

models. The population variable was found to provide a more reliable explanation of residential

heating customer counts than did other available variables. The variable coefficients and the

binary variables included in the Residential Heating Customer models are listed and supported in

Appendix 9. Over the forecast period, the number of Residential Heating customers is projected

to grow at an annual rate of 1.19 percent, as shown by Table III-5 below. Customer counts for a

split year as shown in Table III-5 are the annual average number of customers estimated to be on

line for each year of the forecast.

Table III-5: Residential Heating Customer Forecast

b. Residential Heating Use per Customer Model Results

Economic theory suggests that variation in Residential Heating use per customer is the

result of influences besides weather, for example price and appliance efficiency. The general form

of the selected Residential Heating use per customer models is:

Use per Customer = (a) + (b) * Effective Degree Days + (ci ) * Quarterly Dummiesi + (d) * Real

Price + (ei) * Binary Variablesi + (fi) * Interaction Variablesi

The variable coefficients and the binary and interactive variables that are included in the

Residential Heating Use per Customer models are listed and supported in Appendix 9.

Over the forecast period, the use per customer for the Residential Heating segment is

projected to remain relatively flat with average growth of 0.01 percent per year, as shown in Table

III-6 below.

Split Year Brockton Lawrence Springfield Total

2019-2020 142,003 44,037 90,580 276,620 2020-2021 143,961 44,436 91,068 279,466 2021-2022 145,895 44,847 91,953 282,694 2022-2023 147,842 45,232 93,529 286,603 2023-2024 149,781 45,592 94,601 289,974 CAGR 1.34% 0.87% 1.09% 1.19%


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Table III-6: Residential Heating Use per Customer Model Forecast

(Dth/Customer–Normal Year)

c. Residential Heating Demand Results

The Residential Heating demand forecast for each division was calculated by multiplying

the forecasted number of Residential Heating customers for each quarter by the forecasted

Residential Heating use per customer for that quarter. Over the forecast period, the total demand

from the Residential Heating segment is projected to increase by 1.19 percent per year, as shown

in Table III-7 below.

Table III-7: Residential Heating Demand Forecast (Dth – Normal Year)35

5. Residential Non-Heating Customer Segment

Residential Non-Heating is the smallest CMA customer segment in terms of demand.

From 2014 through 2018, the Residential Non-Heating customer segment represented

approximately 6.7 percent of total customers and approximately less than 1 percent of total actual

demand. Over the same time period, the number of Residential Non-Heating customers decreased

35 All Customer Segment forecast results are before adjustments for DSM savings, which will be discussed in

Section III.B.11.


2019-2020 97.5 104.9 92.6 97.1 2020-2021 97.8 104.9 92.5 97.2 2021-2022 98.0 104.8 92.3 97.2 2022-2023 98.1 104.7 92.1 97.2 2023-2024 98.1 104.7 91.8 97.1 CAGR 0.16% -0.06% -0.19% 0.01%

Gas Year Brockton Lawrence Springfield Total

2019-2020 13,878,289 4,644,206 8,412,107 26,934,603 2020-2021 14,112,748 4,682,280 8,455,764 27,250,792 2021-2022 14,321,764 4,715,477 8,507,088 27,544,329 2022-2023 14,528,129 4,752,519 8,631,876 27,912,524 2023-2024 14,731,462 4,791,470 8,715,904 28,238,837 CAGR 1.50% 0.78% 0.89% 1.19%


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by 3.63 percent per year. As of 2018, the non-heating customer segment represents about

6.8 percent of the total number of residential customers across all three CMA divisions, and the

residential non-heating load is about 1.4 percent of total residential load.

a. Residential Non-Heating Customer Model Results

No economic variables were found to have a meaningful relationship with Residential Non-

Heating customer count. A general decreasing trend in Residential Non-Heat Customers was used

to capture the gradual reduction in this customer category as customers convert from Residential

Non-Heat to Residential Heat. The general form of the selected model for each division can be

represented as:

Residential Non-Heat Customer count = (a) + (bi ) Quarterly Dummy Variablesi + (c)* Trend +

(di ) Binary Variablesi

Over the forecast period, the number of Residential Non-Heating customers is expected to

decline by 3.15 percent per year, as shown by the table below. The Split Year Customer counts

shown in Table III-8 are the average number of customers for the four quarters Q4 through Q3

(October through September).

Table III-8: Residential Non-Heating Customer Model Forecast

b. Residential Non-Heating Use per Customer Model Results

Economic theory suggests that the Residential Non-Heating use per customer may be

dependent on such variables as weather and natural gas price. The general form of the selected

Residential Non-heating use per customer models is:


2019-2020 7,384 2,357 8,887 18,629 2020-2021 7,131 2,288 8,652 18,072 2021-2022 6,878 2,220 8,410 17,508 2022-2023 6,625 2,151 8,172 16,948 2023-2024 6,372 2,083 7,933 16,387 CAGR -3.62% -3.05% -2.80% -3.15%


Page 45 of 88

Use per Customer = (a) + (b)*Effective Degree Days + (ci ) Binary Variablesi + (d) * Real

Price

The variable coefficients and the binary variables that are included in the Residential Non-

Heating Use per Customer models are listed and supported in Appendix 9.

Over the forecast period, the use per customer for the Residential Non-Heating segment is

projected to decrease by 0.04 percent per year, as shown Table III-9, below.

Table III-9: Residential Non-Heating Use per Customer Model Forecast

(Dth/Customer – Normal Year)

c. Residential Non-Heating Demand Results

The Residential Non-Heating demand forecast for each division was calculated by

multiplying the forecasted number of Residential Non-Heating customers for each quarter by the

forecasted Residential Non-Heating use per customer. The Residential Non-Heating customer

demand is projected to decrease by 3.2 percent per year, as shown in Table III-10, below.

Table III-10: Residential Non-Heating Demand Forecast

(Dth – Normal Year)


2019-2020 17.7 20.0 19.5 18.8 2020-2021 17.9 20.0 19.5 18.9 2021-2022 18.0 20.0 19.4 18.9 2022-2023 17.9 20.0 19.4 18.9 2023-2024 17.8 19.9 19.3 18.8 CAGR 0.18% -0.04% -0.23% -0.04%


2019-2020 130,429 47,015 173,146 350,591 2020-2021 127,760 45,701 168,240 341,701 2021-2022 123,820 44,384 163,145 331,349 2022-2023 118,617 42,952 158,101 319,670 2023-2024 113,289 41,443 153,089 307,821 CAGR -3.46% -3.10% -3.03% -3.20%


Page 46 of 88

6. C&I LLF Customer Segment

The C&I LLF customer segment is the second largest CMA customer segment in terms of

demand. From 2014 through 2018, the C&I LLF customer segment represented approximately

8.1 percent of total customers and approximately 29.8 percent of total demand. Over the same

time period, the number of C&I LLF customers increased by 0.73 percent per year.

a. C&I LLF Customer Model Results

Economic theory suggests that the number of C&I LLF customers may be dependent on

such variables as the number of people living in the service territory (i.e., households or

population), employment levels and C&I LLF natural gas prices relative to oil prices. Given C&I

LLF customers in the Company’s service territories tend to be non-manufacturing companies, it

was appropriate to use a broad measure of non-manufacturing employment as a predictor variable.

Therefore, changes in non-manufacturing employment are expected to drive changes in C&I LLF

customer counts. The variable coefficients and the binary and interactive variables that are

included in the C&I LLF Customer models are listed and supported in Appendix 9.

The general form of the selected C&I LLF customer number models is:

Customer Number = (a) + (bi ) * Quarterly Dummiesi + (c) * Employment + (di ) Binary

Variablesi + (ei ) * Interaction Variablesi

Over the forecast period, the number of customers for the C&I LLF customer segment is

projected to remain relatively flat with a net decrease of 0.03 percent per year, as shown in Table

III-11 below. The Split Year Customer counts that are shown in Table III-11 are the average

number of customers for the four quarters Q4 through Q3 (October through September).

Table III-11: C&I LLF Customer Model Forecast


2019-2020 14,686 3,047 8,196 25,929 2020-2021 14,727 3,058 8,170 25,955 2021-2022 14,727 3,059 8,173 25,959 2022-2023 14,705 3,056 8,163 25,925 2023-2024 14,693 3,057 8,144 25,894 CAGR 0.01% 0.08% -0.16% -0.03%


Page 47 of 88

b. C&I LLF Use per Customer Model Results

Economic theory suggests that the C&I LLF use per customer may be dependent on such

variables as weather, employment levels, natural gas prices and gross county product. The variable

coefficients and the binary and interactive variables that are included in the C&I LLF Use per

Customer models are listed and supported in Appendix 9.

The general form of the selected C&I LLF use per customer models is:

Use per Customer = (a) + (bi ) * Quarterly Dummiesi + (c) * Effective Degree Days + (d) * Real

Price + (e) * Interaction Terms + (fi ) * Binary Variablesi

This model form was estimated with the same techniques as for the other customer

segments and is described and supported in Appendix 9.

Over the forecast period, the use per customer for the C&I LLF customer segment is

projected to remain relatively flat with a net decrease of 0.05 percent per year, as shown in Table

III-12, below.

Table III-12: C&I LLF Use per Customer Model Forecast


c. C&I LLF Demand Results

The C&I LLF demand forecast by division was calculated by multiplying the forecasted

number of C&I customers for each quarter by the forecasted C&I LLF use per customer. Over the

forecast period, the demand for the C&I LLF customer segment is projected to decrease by 0.08

percent per year, as shown in Table III-13, below.


2019-2020 547.9 769.3 661.5 609.9 2020-2021 548.3 774.6 662.5 610.9 2021-2022 548.1 772.6 661.9 610.4 2022-2023 547.8 769.6 661.0 609.6 2023-2024 547.4 765.8 659.8 608.6 CAGR -0.02% -0.11% -0.06% -0.05%


Page 48 of 88

Table III-13: C&I LLF Demand Forecast (Dth – Normal Year)

7. C&I HLF Customer Segment

The C&I HLF customer segment is the smallest CMA customer segment in terms of

number of customers, but the third largest in terms of demand. From 2014 through 2018, the C&I

HLF customer segment was approximately 1.7 percent of total customers and approximately 20.2

percent of total actual demand.

a. C&I HLF Customer Model Results

Economic theory suggests that C&I HLF customer count may be dependent on such

variables as weather, manufacturing employment levels, labor force, natural gas prices and

measures of output. Additionally, historically customers assigned to the C&I HLF class may be

reclassified as C&I LLF following a colder-than-normal winter and vice versa following a warmer-

than-normal winter. A lagged Q4 to Q3 billing EDD deviation from normal billing EDD was

derived to capture this dynamic for each division. The variable coefficients and the binary and

interactive variables that are included in the C&I HLF Customer models are listed and supported

in Appendix 9.

The general form of the selected C&I HLF Customer models is:

Customer Number = (a) + (bi ) * Quarterly Dummiesi + (c) * Lagged EDD deviation from

Normal + (d) * Real Gross County Product + (e) * Manufacturing Employment + (fi) * Binary

Variablesi

Over the forecast period, the number of customers in the C&I HLF segment is projected to

decrease by 0.13 percent per year, as shown in Table III-14, below.


2019-2020 8,103,070 2,371,531 5,463,140 15,937,741 2020-2021 8,133,647 2,395,417 5,448,284 15,977,347 2021-2022 8,133,022 2,390,210 5,456,974 15,980,206 2022-2023 8,117,883 2,378,689 5,440,551 15,937,123 2023-2024 8,104,951 2,367,036 5,416,196 15,888,184 CAGR 0.01% -0.05% -0.22% -0.08%


Page 49 of 88

Table III-14: C&I HLF Customer Forecast

b. C&I HLF Use per Customer Model Results

Economic theory suggests that the C&I HLF use per customer may be dependent on such

variables as weather, employment levels, natural gas prices and gross county product. The variable

coefficients and the binary and interactive variables that are included in the C&I HLF use per

customer models are listed and supported in Appendix 9.

The general form of the selected C&I HLF use per customer models is:

Use per Customer = (a) + (bi ) * Quarterly Dummiesi + (c) * Effective Degree Days + (d) *

Lagged EDD deviation from Normal + (e) * Real Price + (f) * Interaction Terms + (gi ) * Binary

Variablesi

This model form was estimated with the same techniques as for the other customer

segments and is described and supported in Appendix 9.

Over the forecast period, the use per customer for the C&I HLF customer segment is

projected to increase by 1.05 percent per year, as shown in Table III-15, below.

Table III-15: C&I LLF Use per Customer Model Forecast



2019-2020 2,648 771 1,722 5,141 2020-2021 2,644 774 1,713 5,131 2021-2022 2,640 776 1,707 5,123 2022-2023 2,637 778 1,702 5,118 2023-2024 2,633 782 1,699 5,114 CAGR -0.14% 0.36% -0.33% -0.13%


2019-2020 1,492.7 1,896.6 2,946.3 2,040.1 2020-2021 1,506.4 1,958.0 2,981.4 2,067.1 2021-2022 1,530.3 2,006.7 3,035.6 2,104.0 2022-2023 1,518.7 2,035.8 3,067.3 2,112.4 2023-2024 1,509.9 2,052.3 3,117.1 2,126.8 CAGR 0.29% 1.99% 1.42% 1.05%


Page 50 of 88

c. C&I HLF Demand Results

The C&I HLF demand forecast for each division was calculated by multiplying the

forecasted number of C&I HLF customers for each quarter by the forecasted C&I HLF use per

customer. Over the forecast period, the demand for the C&I HLF customer segment is projected

to increase by 0.89 percent per year, as shown in Table III-16, below.

Table III-16: C&I HLF Demand Forecast (Dth – Normal Year)

8. Capacity Exempt Transportation Demand

The Company’s Distribution and Default Service Terms and Conditions provide for the

assignment of a share of all CMA resource contracts that are eligible for assignment to customers

that received bundled sales service after February 1, 1999.36 All other customers that (a) received

bundled sales service at some time, but converted to unbundled transportation service prior to

February 1, 1999, or (b) have never received bundled sales service, can elect to be “Capacity

Exempt” transportation customers.37 The Company must have adequate resources to meet the

projected demands of bundled sales customers and non-capacity exempt customers (i.e., Planning

Load); as directed by the Department, CMA does not plan its resources to meet the projected

demand of capacity exempt customers.

The projected Planning Load by division is determined by subtracting forecast capacity-

exempt transportation volumes by division from total projected customer segment volumes.

36 CMA’s customers that were taking firm transportation service prior to February 1, 1999, were “grandfathered” and

thus were exempt from the provisions of the Company’s mandatory capacity assignment requirements.

37 Added load of existing capacity-assigned customers also can and usually does become capacity exempt load, unless the customer, or third-party supplier on behalf of the customer, requests additional assigned capacity to meet such load additions.


2019-2020 3,953,012 1,466,553 5,086,416 10,505,981 2020-2021 3,988,251 1,519,339 5,110,008 10,617,598 2021-2022 4,039,608 1,560,413 5,184,944 10,784,965 2022-2023 4,001,670 1,587,215 5,226,746 10,815,630 2023-2024 3,975,898 1,606,457 5,301,893 10,884,248 CAGR 0.14% 2.30% 1.04% 0.89%


Page 51 of 88

CMA’s capacity-exempt load had been decreasing over time but in recent years capacity-exempt

loads have stabilized. Anecdotal evidence indicated that some customers had voluntarily given up

their capacity exempt status because CMA’s firm gas sales service was somewhat less directly

linked to the delivered gas price volatility experienced in Massachusetts as compared to the market

based natural gas prices marketers were charging; other customers lost their capacity exempt status

because they failed to schedule transportation gas, and thus received firm gas sales service.

However, the significant reverse-migration of capacity exempt customers back to planning load

that began five years ago appears to have ended.

The general form of the Capacity Exempt demand model for each division can be

represented as:

Capacity Exempt Transportation Demand = (a) + (b) * EDD

The variable coefficients and the binary variables that are included in the Capacity Exempt

Customers and Demand models are listed and supported in Appendix 9.

Over the forecast period, the Capacity Exempt transportation Demand is projected to

remain relatively flat with a net decrease of 0.01 percent per year, as shown in Table III-17.

Table III-17: Capacity Exempt Transportation Demand Forecast

9. Company Use and Losses

The Company Use gas forecast was developed by division using ten years of actual data.

The annual level was set equal to the ten-year average while the monthly levels were calculated

with an allocation percentage. An allocation percentage was used rather than the calculated

average of the monthly values because of the variability of the data. The allocation percentage


2019-2020 1,732,024 927,763 3,812,928 6,472,715 2020-2021 1,730,010 927,763 3,812,928 6,470,701 2021-2022 1,729,142 927,763 3,812,928 6,469,833 2022-2023 1,728,768 927,763 3,812,928 6,469,458 2023-2024 1,728,606 927,763 3,812,928 6,469,297 CAGR -0.05% 0.00% 0.00% -0.01%


Page 52 of 88

was calculated as the percent occurring in a given month of the ten-year average annual level

calculated excluding observations that were more than two standard deviations from the average

of the monthly values. See Table III-18.

Table III-18: Company Use Gas Forecast in Dth

The Brockton data, graphed in Figure III-1, is included as an example of the variability of

the Company Use data. There is one line for each year of data and the forecast is represented as

the bold line with symbols.

Figure III-1: Company Use Example

Additional details of the Company Use forecast are provided in Appendix 10.

LAUF gas volume is calculated using the average of the annual LAUF percentages for the

total the Company reported to the Department in the past ten years. The same LAUF percentage


2019-2020 24,414 32,536 32,346 89,296 2020-2021 24,414 32,536 32,346 89,296 2021-2022 24,414 32,536 32,346 89,296 2022-2023 24,414 32,536 32,346 89,296 2023-2024 24,414 32,536 32,346 89,296 CAGR 0.00% 0.00% 0.00% 0.00%


Page 53 of 88

is applied to all divisions. Company level data was used to calculate a total LAUF gas volume

because a reliable reporting system for company-wide LAUF was developed for that purpose and

has been in place for many years. Comparable division level data is not produced by this system.

The forecast percentages are included in Table III-19.

Table III-19: Unaccounted For Gas Forecast Percentages

10. Conversion of Billing Period Volumes to Calendar Period Volumes

CMA’s volume-per-customer and volume models are based on quarterly billing data that

are the sum of billing month data. Billing quarter volume forecasts are summed into billing

seasons to develop gas year totals on a billing period basis. Q4 forecasted volumes were split

between October and November/December by division and by customer segment (and capacity

exempt) based on historical October as a percent of Q4 volumes. Billing period winter is the sum

of Nov/Dec and Q1, while billing period summer is the sum of Q2, Q3 and October.

Billing season volume is converted to calendar season volume with the unbilled forecast.

Unbilled volume forecasts are derived from seasonal unbilled models for each division.

The general form of the unbilled volume model for each division can be represented as:

Unbilled Volume = (a) + (b) * Unbilled Effective Degree Days

For each season, billing volumes plus the unbilled balance minus the unbilled balance from

the previous season equals calendar season volumes.

Total *

Company

LAUF %

2009 1.3%

2010 1.4%

2011 0.9%

2012 1.5%

2013 2.2%

2014 1.6%

2015 1.7%

2016 1.7%

2017 2.0%

2018 2.2%

Average 1.7%


Page 54 of 88

Unbilled capacity exempt transportation volume is calculated by multiplying unbilled total

volumes times the ratio of billing season capacity exempt transportation volume to billing season

total volume.

11. CMA’s Energy Efficiency Programs

The customer segment demand forecasts discussed above include the effects of historical

energy efficiency savings and expected future energy efficiency savings at historical levels. The

forecast assumes that EE savings will continue to accumulate each year at levels similar to

historical EE savings achieved. The increase in projected EE savings compared to historical levels

approved in the 2019-2021 EE Plan represents a departure from increases in prior EE plans and

historic data. As shown in Table III-20, the savings goals in the 2019-2021 EE Plan represent a

70% increase over the Company’s goals in its 2016-2018 EE Plan.

Table III-20: Energy Efficiency Three-Year Plan Therm Savings Goals and Achievements

As demonstrated in Table III-20, the 2019 savings goal is a 61% increase over Year 1 of

the 2016-2018 EE Plan (i.e., 2016) goal, the 2020 savings goal is a 75% increase over Year 2 of

the 2016-2018 EE Plan (i.e., 2017) goal, and the 2021 savings goal is a 74% increase over Year 3

of the 2016-2018 EE Plan (i.e., 2018) goal. The 2019-2021 savings goals are also an average of

53% higher than the record actual savings achieved by CMA in 2018.

Given the nature of this increase over historical achievement and prior savings goals,38

CMA has relied on the historical actual energy savings from its EE programs to estimate forecasted

38 No other LDC experienced an increase to this degree. The next highest savings goals increase is 13% from the 2016-

2018 EE Plan to the 2019-2021 EE Plan.

2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

Year 1 Year 2 Year 3 Year 1 Year 2 Year 3 Year 1 Year 2 Year 3 Year 1 Year 2 Year 3

Annual Therm Savings Goals (Adjusted Gross)

Residential 1,001,641 1,045,623 1,265,977 1,997,564 2,039,975 2,045,484 1,878,223 1,882,062 1,920,289 2,331,598 2,828,189 2,850,577

Low‐Income 216,010 189,119 217,471 245,314 269,646 296,410 356,228 352,557 352,557 472,552 510,659 463,961

C&I 1,608,632 1,811,942 1,997,381 2,202,255 2,204,549 2,223,038 2,242,222 2,248,246 2,267,146 4,384,822 4,497,617 4,575,773

Total 2,826,283 3,046,684 3,480,829 4,445,133 4,514,170 4,564,932 4,476,673 4,482,865 4,539,992 7,188,972 7,836,464 7,890,311 Average

Percent Increase from Same Year, Previous Plan 61% 75% 74% 70%

Percent Increase from 2018 Annual Savings 44% 57% 58% 53%

Annual Therm Savings Actual (Adjusted Gross)

Residential 975,711 1,073,209 1,202,498 1,573,107 1,662,472 2,102,029 2,122,428 1,881,164 2,372,211 TBD TBD TBD

Low‐Income 123,145 146,389 256,493 345,747 369,312 323,890 337,599 353,200 366,719 TBD TBD TBD

C&I 1,629,638 1,398,607 1,627,080 2,172,313 2,255,400 1,306,769 1,488,654 1,097,805 2,261,629 TBD TBD TBD

Total 2,728,494 2,618,205 3,086,071 4,091,167 4,287,184 3,732,688 3,948,680 3,332,169 5,000,559 TBD TBD TBD


Page 55 of 88

levels of EE savings in this F&SP. The Company will review its full year 2019 achieved savings

against 2019 savings goals and make a supplemental filing in January 2020 based upon its

review.39

12. Throughput Forecast

o The normal year throughput forecast was calculated by summing the normal year

forecasts for the four customer segments and adding Company Use, lost and unaccounted for gas

volumes, and adjusting for unbilled.

o The total number of customers is projected to increase over the forecast period by

0.84 percent per year, as shown in Table III-21, below. Split Year customer counts shown in

Table III-20 are the average number of customers for the four quarters Q4 through Q3 (October

through September).

Table III-21: Total Company Customer Forecast

As shown in Table III-22, the total throughput adjusted for Company Use, losses, and

unbilled is projected to increase by 0.73 percent per year over the forecast period.

Table III-22: Firm Throughput Forecast (Dth – Normal Year)

39 Through September 2019, the Company has achieved 35 percent of 2019 savings goals.


2019-2020 166,722 50,211 109,386 326,319 2020-2021 168,463 50,556 109,604 328,624 2021-2022 170,140 50,901 110,243 331,284 2022-2023 171,810 51,218 111,565 334,594 2023-2024 173,479 51,513 112,376 337,368 CAGR 1.00% 0.64% 0.68% 0.84%


2019-2020 26,528,797 8,706,101 19,490,106 54,725,004 2020-2021 26,831,417 8,821,445 19,538,392 55,191,254 2021-2022 27,091,536 8,890,333 19,670,435 55,652,304 2022-2023 27,242,116 8,942,081 19,818,003 56,002,200 2023-2024 27,404,100 8,987,871 19,950,001 56,341,972 CAGR 0.81% 0.80% 0.58% 0.73%


Page 56 of 88

The throughput shown in this section is total load rather than planning load. The planning

load forecast is shown in Table III-23 and is assembled as shown in Table 23:

Table III-23: Firm Planning Load Forecast (Dth – Normal Year)

Table III-24: Calculation of Forecast Planning Load by Division

Category Term and units Calculation details 1. Residential Heating, Residential Non-

Heating, C&I LLF and C&I HLF Billing quarter customers and UPC (Dth/cust)

Forecast customers x forecast UPC

2. Residential Heating, Residential Non-Heating, C&I LLF and C&I HLF

Billing season demand (Dth)

Winter: Nov/Dec + Q1 Summer: Q2 + Q3 + Oct

3. Capacity Exempt Billing quarter demand (Dth)

Capacity Exempt Model

4. Capacity Exempt Billing season demand (Dth)

Winter: Nov/Dec + Q1 Summer: Q2 + Q3 + Oct

5. Company Use Billing month demand (Dth)

Company Use model

6. Billing Season Firm Demand Billing season demand (Dth)

Calculated from models using seasonal values from (2) + (5)

7. Billing Season Planning Load Billing season demand (Dth)

Calculated from models using seasonal values from (6) – (4)

8. Unbilled Seasonal unbilled (Dth)

Seasonal unbilled model

9. Calendar Season Firm Demand Calendar season demand (Dth)

Billing season firm demand + Unbilled (t) – Unbilled (t-1)

10. Capacity Exempt unbilled Seasonal unbilled (Dth)

Total Unbilled * (Billed Capacity Exempt / Billed Total)

11. Capacity Exempt Calendar Season Demand

Calendar season demand (Dth)

Billing season Capacity exempt + Unbilled CE (t) – Unbilled CE (t-1)

12. Lost and Unaccounted for Gas Calendar season demand (Dth)

Loss factors applied to Total Firm Demand (9) and Capacity Exempt Demand (11)

13. Planning Load Calendar season demand (Dth)

(9) – (11) + (12)


2019-2020 24,781,189 7,770,257 15,640,048 48,191,494 2020-2021 25,086,197 7,885,484 15,688,724 48,660,405 2021-2022 25,347,401 7,954,224 15,820,341 49,121,966 2022-2023 25,498,595 8,005,872 15,967,659 49,472,126 2023-2024 25,660,807 8,051,601 16,099,411 49,811,818 CAGR 0.88% 0.89% 0.73% 0.83%


Page 57 of 88

C. ECONOMIC GROWTH SCENARIOS

In addition to the base case planning load results discussed in Sections III.A and B,

optimistic (high growth) demand forecasts were prepared to provide a range of outcomes for the

planning load forecast. The purpose of the high growth scenario is to evaluate the Company’s

resource plan under conditions in which higher than expected demand is experienced. In previous

F&SP filings, the Company also created and tested a pessimistic (low) demand forecast, but since

the low case scenario has lower demand than the base case, it does not serve as a test of the

portfolio’s ability to serve load during unexpected conditions. Therefore, to increase the efficiency

associated with developing and reviewing the F&SP, and consistent with other Massachusetts

LDCs, the Company has only presented base case and high case economic growth scenarios in this

F&SP filing.

The high growth case is based on variable values from optimistic economic scenarios that

were provided by the Company’s economic data vendor, IHS Global Insight, Inc and EIA.40 The

high growth case was developed by re-running the models described above with the optimistic

scenario data in the forecast period. A summary of the high growth forecast results of annual

planning load for the five-year forecast period is shown in Table III-25 below.

Table III-25: Planning Load High Growth Scenario (Dth – Normal Year)

D. DAILY PLANNING LOAD FORECAST INPUTS TO SENDOUT®

After the demand forecast is completed, customer requirements are input into the

Company’s portfolio optimization model. CMA has used the SENDOUT® model to provide

quantitative support for gas supply decisions and assist in evaluating and selecting an optimal

40 The optimistic scenarios were provided in data sets specially prepared by IHS Global Insight for this purpose. The

county level optimistic forecasts correspond to IHS Global Insight’s optimistic national views.


2019-2020 25,356,469 8,106,742 16,242,120 49,705,331 2020-2021 25,793,546 8,251,066 16,509,655 50,554,267 2021-2022 26,157,794 8,351,565 16,823,187 51,332,546 2022-2023 26,410,852 8,434,991 17,086,719 51,932,561 2023-2024 26,671,085 8,508,743 17,377,625 52,557,453 CAGR 1.27% 1.22% 1.70% 1.40%


Page 58 of 88

supply portfolio among a range of available alternatives since 1995. As discussed further in

Section IV.C of this Report, SENDOUT® is a multi-period optimization model that utilizes a linear

programming algorithm to identify the portfolio that will satisfy CMA’s firm sales at the least cost.

The forecast demand that is specified in the SENDOUT® model is the daily Planning Load

forecast.

While the methodology for determining daily normal year, design year and cold snap EDD

is the same as used in previous F&SP filings, the daily planning load forecast approach described

in more detail below and in Appendices 13 and 14 is different from the methodology used in the

last F&SP filing to determine daily load for SENDOUT®. In previous F&SPs, the Company

allocated quarterly planning load forecast results to months, and SENDOUT® was utilized to

develop monthly base load and heat load factors for the various economic scenarios. These

monthly base load and heat load factors were applied to daily weather patterns for normal year,

design year and cold snap to produce daily planning load forecasts against which the resource

portfolio could be tested. In addition, in previous F&SP’s the Company developed a separate

model to predict planning load demand on design day that was directly entered into SENDOUT®.

In this F&SP, a daily planning load forecast is developed using a daily regression analysis to

provide daily results under various weather scenarios. Daily gate-station meter data by division is

used to develop a daily planning load shape for each division using regression modeling and

including variables such as weather and other date/season related measures. Design day is

assumed to be a part of design year, so the same daily planning load model produces results for

design day as well. These daily planning load results are directly entered into SENDOUT® for

further resource planning modeling. This approach is more efficient and accurate than previous

methods of dividing demand into base load and heating load factors.

In summary, the process of developing data for SENDOUT® related to Daily Planning

Load has three steps:

1. Determining the Daily EDD Pattern,

2. Developing daily Planning Load shape models to shape gas year Planning Load

forecast results under various weather conditions, and

3. Entering Daily Planning Load demands directly into SENDOUT®


Page 59 of 88

1. Daily EDD Pattern

The first step in the process is to determine a daily EDD pattern, by month and division,

which reflects the day-to-day randomness seen in actual weather, but matches the number of EDD

on which the Planning Load forecast is based. If the daily average EDD for each day was used to

create the daily EDD pattern, it would produce an artificially flattened year. Therefore, an EDD

pattern is built for Normal year and Design year for each of CMA’s three divisions based on a

daily EDD pattern experienced during an actual historical month. For Normal year, the monthly

normal EDD shown in Table III-27 form the basis for the monthly EDD patterns by division. For

example, for the Brockton Division the normal calendar EDD for January are 1,224. The closest

January with EDD similar to 1,224 EDD (i.e., January 1999 with 1,195 EDD in Brockton in this

case) is used to create a daily ratio to distribute the normal EDD into the daily shape for January.

This ensures the daily EDD matches the desired EDD level but maintains a typical daily pattern.

Table III-26 illustrates this process for Brockton.

Table III-26: Sample of Daily Temperature Pattern – Brockton

January Division = Brockton Average EDD = 1,224

Day EDD (1999) Daily EDD Ratio to

Monthly Sum Normal EDD Daily Pattern

1 60 0.05 61 2 50 0.04 51 3 30 0.03 31 4 45 0.04 46 5 47 0.04 48 6 40 0.03 41 7 48 0.04 49 8 35 0.03 36 9 31 0.03 32 10 45 0.04 46 11 45 0.04 46 12 27 0.02 28 13 49 0.04 50 14 56 0.05 57 15 37 0.03 38 16 34 0.03 35 17 30 0.03 31


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18 25 0.02 26 19 30 0.03 31 20 33 0.03 34 21 28 0.02 29 22 28 0.02 29 23 23 0.02 23 24 22 0.02 22 25 34 0.03 35 26 35 0.03 36 27 33 0.03 34 28 46 0.04 47 29 49 0.04 50 30 53 0.04 54 31 47 0.04 48

Total 1,195 100 1,224 This process is repeated for each month, for each division, to create the daily EDD for normal year.

This process is also repeated for each month for each division to create the daily EDD pattern for

design year.

2. Daily Planning Load Shape Model

Historical Daily Planning Load and Daily EDDs by division for the most recent 12 month

period were used to create the Daily Planning Load shape model. For Brockton and Springfield,

April 1, 2018 through March 31, 2019 was used. For Lawrence, September 1, 2017 through

August 31, 2018 was used to exclude any impact from the Merrimack Valley incident. The general

form of the selected Daily Planning Load model is:

Daily Planning Load = (a) + (b) * EDD + (c) * Prior Day EDD + (d) * Second Prior Day EDD

+ (e) * EDD Base X + (f) *Interaction Terms + (gi ) * Binary Variablesi

The specific variable coefficients and the binary variables that are included in the daily

planning load shape models are listed and supported in Appendix 13.

The daily planning load shape model was used to generate a daily planning load shape by

division for a gas year assuming normal weather, design weather, and cold snap weather conditions

by entering the daily EDD pattern associated with each weather condition into the daily planning

load shape model.


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3. Daily Planning Load for SENDOUT®

The division-specific daily planning load shapes for each weather condition produced by

the daily planning load models were applied to the corresponding annual planning load forecasts

discussed in Section III.A and III.B above to produce daily planning load forecasts for each

weather condition by division. The annual planning load forecasts provided in Table III-22

determined the level of the forecast, and the daily planning load shape model determined the daily

planning load within the year. These daily Planning Load results by division were directly entered

into SENDOUT® for use in evaluating the Company’s resource portfolio.

E. PLANNING STANDARDS AND DESIGN FORECASTS

1. Introduction

This F&SP is the result of CMA’s planning process, which is designed to ensure that

resources are adequate to meet customer requirements under various weather conditions (e.g.,

normal year, design year (including design day), and cold snap) and economic growth scenarios

(e.g., base case and high growth).

2. Weather Data

CMA’s primary planning standards are weather-related. Design planning standards are

established through statistical methods using a weather database of the Company’s entire available

division-specific EDDs. With daily EDDs beginning in 1967, this database serves as the basis for

developing planning standards that are related to design, extreme weather, conditions.

3. Normal Year

Normal year is the typical or average weather scenario for which the Company expects to

provide resources/capacity for its customers. To determine normal year conditions, CMA sums

the number of EDDs for each month and calculates the mean monthly EDD for each division using

the data from January 1999 to December 2018. The mean monthly EDDs are summed, by division,

to arrive at the normal year EDDs as shown in Table III-27.


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Table III-27: Normal Year EDDs41

4. Design Standards and Design Forecast

The design day and design year standards represent extreme winter weather conditions that

have a statistically defined probability of occurring on a very infrequent basis; the design day and

design year standard is used to assess the Company’s plans to provide reliable service under

extremely cold weather conditions. In Bay State Gas Company, D.T.E. 06-84 (2007), the

Department approved a change to CMA’s design day and design year planning standards from 1-

in-25 years to 1-in-33 years, and as such the Company is using this planning standard for its sixth

consecutive F&SP filing. In the past, the Company performed a cost-benefit analysis that

examined the net benefit of maintaining capacity to meet its 1-in-33 year level of reliability as

compared with the potential damage costs associated with service disruptions that could be

incurred; however, a cost-benefit analysis has not been filed in this F&SP, consistent with the

Department’s decision in D.P.U. 16-40, which states that “the Department will no longer require

the Company to file a cost-benefit analysis with its future Plans.”42 CMA’s design planning

standards for design day and design winter are derived by applying a 1-in-33 year probability of

occurrence to a t-distribution.

41 As discussed above, to reflect the day-to-day randomness seen in actual EDD data, daily EDD patterns were

developed for each month by identifying a historical actual month with total EDDs similar to the average for that month, and then the daily EDDs were proportionately scaled so the total for the month equaled the monthly average. This process for each month resulted in a daily string of EDDs that represented a typical or normal year.

42 NSTAR Gas Company d/b/a/ Eversource Energy, D.P.U. 16-40, at 9-10 (2017).

Brockton Lawrence SpringfieldJanuary 1,224 1,289 1,288 February 1,039 1,096 1,093 March 918 964 915 April 550 600 514 May 257 297 203 June 62 82 38 July 6 10 3 August 8 17 6 September 95 125 86 October 409 456 411 November 703 757 722 December 1,055 1,118 1,080 Total Annual 6,326 6,811 6,359


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The design day calculation is applied to the peak EDD experienced each year. CMA’s

design day standard was determined to be 78 EDDs for Brockton, 78 EDDs for Springfield and

80 EDDs for Lawrence, based on the distribution of peak day EDDs for 1967 to 2019, and a 1-in-

33-year probability of occurrence.43

CMA’s design winter standard was calculated based on the distribution of winter EDD for

1967 to 2019 and a 1-in-33-year probability of occurrence. The ratio of the 1-in-33-year level of

heating season EDD to average heating season EDD is applied to each month of the November-

March heating season to derive Design year EDDs by month. The analysis is based on data for

the winter beginning November 1967 through the winter beginning November 2018. Design Year

EDD in the non-winter months (i.e., April-October) are assumed to be the same as Normal Year.

Design year EDDs are shown in Table III-28. The derivation of the Design Day EDDs and Design

Winter EDDs are shown in Appendix 7.

Table III-28: Design Year EDDs @ 1 in 33

As discussed above, to reflect the day-to-day randomness seen in actual EDD data, daily

EDD patterns were also developed for each month in Design Year by identifying a historical actual

month with total EDDs similar to the design EDD for that month, and then the daily EDDs were

proportionately scaled so the total EDD equaled the design total EDD for the month. Repeating

43 The full weather data set was used for the Lawrence Division since the Merrimack Valley incident did not affect the

weather data.

Brockton Lawrence SpringfieldJanuary 1,452 1,511 1,499 February 1,232 1,285 1,272 March 1,089 1,130 1,065 April 550 600 514 May 257 297 203 June 62 82 38 July 6 10 3 August 8 17 6 September 95 125 86 October 409 456 411 November 834 887 840 December 1,251 1,311 1,257 Total Annual 7,245 7,711 7,194


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this process for each month resulted in a daily string of EDDs that represents a design year. An

exception to this process was made for design day. Design day was assumed to occur in a design

year January to ensure design day had a high impact on daily planning load. To further increase

the impact of design day load, design day was assumed to occur on a workday and to occur after

another relatively cold day to capture the increased load related to the prior day EDD. Based on

the assumption that Design Day occurs on January 16, the remaining days in design year January

were adjusted slightly downward so the daily EDD pattern for design January equals total design

monthly January EDD.

5. Design Year Planning Load Requirements

Based on the Design Year daily EDDs forecast, design year and design day demand for

each division is derived from the daily planning load shape model, as discussed in Appendix 13

Please see Tables III-29 and III-30 for Design Day and Design Year Planning Load forecasts,

respectively.

Table III-29: Design Day Planning Load Forecast (Dth – Design Day)

Table III-30: Design Year Forecast

Planning Load Model Forecast (Dth – Design Year) - Brockton

Winter Season Brockton Lawrence Springfield Total Dth2019-2020 274,763 82,631 160,088 517,4822020-2021 278,072 83,774 160,408 522,2552021-2022 280,908 84,464 161,711 527,0832022-2023 282,643 85,001 163,220 530,8642023-2024 284,487 85,493 164,524 534,503CAGR 0.87% 0.85% 0.69% 0.81%

Gas YearResidential


Firm Unbilled and

Losses

Total Firm After

Unbilled and Losses

Capacity Exempt Demand

After Unbilled and Losses

Total Planning

Load

2019-2020 15,935,889 13,338,210 24,414 493,656 29,792,170 1,863,057 27,929,112 2020-2021 16,194,244 13,408,057 24,414 499,186 30,125,901 1,860,366 28,265,535 2021-2022 16,425,220 13,459,320 24,414 503,942 30,412,895 1,859,114 28,553,782 2022-2023 16,652,357 13,404,799 24,414 506,850 30,588,421 1,858,283 28,730,138 2023-2024 16,876,221 13,364,950 24,414 509,951 30,775,536 1,858,007 28,917,530 CAGR 1.44% 0.05% 0.00% 0.82% 0.82% -0.07% 0.87%

Brockton


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Planning Load Model Forecast (Dth – Design Year) – Lawrence

Planning Load Model Forecast (Dth – Design Year) - Springfield

Planning Load Model Forecast (Dth – Design Year) – All Divisions

The normal year, design year (including design day) and cold snap planning loads by

division were compared against the Company’s resources using SENDOUT® and will be

discussed in Section IV.C. Figure III-2 illustrates the load duration curves for the 2019/20

forecasted planning load under normal and design year conditions.

Gas YearResidential


Firm Unbilled and

Losses

Total Firm After

Unbilled and Losses

Capacity Exempt


Losses

Total Planning

Load

2019-2020 5,310,334 4,255,884 32,536 161,731 9,760,485 995,675 8,764,810 2020-2021 5,351,449 4,334,281 32,536 163,745 9,882,011 995,879 8,886,132 2021-2022 5,387,412 4,370,522 32,536 164,962 9,955,432 996,123 8,959,309 2022-2023 5,428,424 4,385,675 32,536 165,908 10,012,543 996,286 9,016,258 2023-2024 5,471,944 4,393,550 32,536 166,774 10,064,804 996,383 9,068,421 CAGR 0.75% 0.80% 0.00% 0.77% 0.77% 0.02% 0.85%

Lawrence

Gas YearResidential


Firm Unbilled and

Losses

Total Firm After

Unbilled and Losses

Capacity Exempt


Losses

Total Planning

Load

2019-2020 9,619,947 11,434,314 32,346 355,292 21,441,899 3,994,037 17,447,862 2020-2021 9,664,138 11,423,607 32,346 355,856 21,475,948 3,993,209 17,482,738 2021-2022 9,718,773 11,509,396 32,346 358,222 21,618,738 3,994,016 17,624,722 2022-2023 9,856,665 11,533,742 32,346 360,956 21,783,709 3,994,505 17,789,204 2023-2024 9,948,437 11,582,145 32,346 363,318 21,926,245 3,994,980 17,931,265 CAGR 0.84% 0.32% 0.00% 0.56% 0.56% 0.01% 0.69%

Springfield

Gas YearResidential


Firm Unbilled and

Losses

Total Firm After

Unbilled and Losses

Capacity Exempt


Losses

Total Planning

Load

2019-2020 30,866,170 29,028,408 89,296 1,010,680 60,994,554 6,852,770 54,141,784 2020-2021 31,209,831 29,165,945 89,296 1,018,788 61,483,860 6,849,455 54,634,405 2021-2022 31,531,405 29,339,238 89,296 1,027,126 61,987,065 6,849,253 55,137,812 2022-2023 31,937,446 29,324,217 89,296 1,033,714 62,384,673 6,849,073 55,535,600 2023-2024 32,296,602 29,340,645 89,296 1,040,042 62,766,585 6,849,370 55,917,215 CAGR 1.14% 0.27% 0.00% 0.72% 0.72% -0.01% 0.81%

Total


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Figure III-2: 2019/20 Load Duration Curve – Planning Load

6. Cold Snap

In addition to analyzing design year and design day, the system’s ability to meet customer

requirements is tested during a prolonged cold period (i.e., a cold snap). A cold snap is defined as

the 24 consecutive day period with the greatest total EDDs in the entire historical data set (i.e.

1967 to the present).

By that criterion, the coldest 24-day period in the Company’s history occurred from

January 7, 2004 through January 30, 2004. The Company used this actual 24-day period to test a

cold snap impact on the adequacy of its resources. The total number of EDDs was 1,328, which

yields a daily average of over 55 EDDs.

For the SENDOUT® analysis, the cold snap was assumed to occur in a normal year January

to ensure the cold snap had the greatest impact on load. Based on the assumption that the cold

snap started on January 7, the cold snap planning load requirements were calculated as part of the

normal year planning load requirements by division using the daily planning load shape models.

Table III-31 demonstrates the cold snap forecast planning load over the forecast period.


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Table III-31: Planning Load Model Forecast (Dth – Cold Snap)

The planning load forecast results for a cold snap are about 56 percent higher than the

normal year results for that same 24-day time period. Figure III-3 illustrates the load duration

curves for the 2019/20 forecast planning load under normal year, normal year including cold snap,

and design year weather conditions.

Figure III-3: 2019/20 Load Duration

Gas Year Brockton Lawrence Springfield Total Dth2019-2020 26,344,570 8,191,542 16,411,312 50,947,423

2020-2021 26,668,819 8,313,016 16,462,389 51,444,224

2021-2022 26,946,502 8,385,483 16,600,497 51,932,482

2022-2023 27,107,235 8,439,931 16,755,079 52,302,245

2023-2024 27,279,680 8,488,140 16,893,328 52,661,147

CAGR 0.88% 0.89% 0.73% 0.83%


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IV. RESOURCE PORTFOLIO ANALYSES

A. CMA’S DECISION-MAKING PROCESS

The Department has reviewed and approved the Company’s prior F&SP filings, the last

four of which were docketed as, D.P.U. 11-89, D.P.U. 13-161, D.P.U. 15-143 and D.P.U. 17-166.

The Department summarized its findings following its investigation of CMA’s last F&SP,

D.P.U. 17-166, as follows:

The Company has provided evidence that it has a resource-planning process that ensures its ability to acquire least-cost supplies … The Company has demonstrated that its supply portfolio is adequate to satisfy forecast normal-year, design–year, and design–day sendout requirements under low-growth, high-growth, and base-case conditions throughout the forecast period with the acquisition of incremental resources … Accordingly, the Department finds that Bay State has established that it possesses adequate supplies to meet the expected forecast normal-year, design–year, and design–day sendout requirements throughout the forecast period. Finally, based on the Company’s analysis, the Department finds that Bay State has demonstrated that it has adequate supplies to meet firm sendout requirements during a prolonged cold snap.

D.P.U. 15-166, at 46-50.

Also, the Department has reviewed CMA’s planning process and associated results in its

various decisions approving specific resource acquisitions, most recently in D.P.U. 10-49,

D.P.U. 10-65, D.P.U. 10-134, D.P.U. 12-04, D.P.U. 12-64, D.P.U. 13-158, D.P.U. 15-39,44

D.P.U. 15-142, D.P.U. 15-170, D.P.U. 15-175, D.P.U. 17-85, D.P.U. 17-97 and D.P.U. 17-166.

In each of these proceedings, the Department found that the Company’s demand forecasting and

supply planning processes are consistent with the Department’s requirements. In these various

Department decisions, the Department found that CMA’s resource decisions contributed to

meeting the Company’s interrelated goals of flexibility, diversity, viability and least-cost. Further,

in each of the above proceedings, the Department found CMA’s planning process to be reasonable

and appropriate.

44 The Northeast Energy Direct project was withdrawn by TGP after Department approval.


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As explained in this section, CMA’s resource planning process is largely unchanged since

its previously approved Plan noting that the process appropriately takes into consideration

changing market dynamics at the wholesale and retail levels.

1. CMA’s Planning Goals

CMA’s decision-making process requires the Company to establish appropriate goals and

objectives, consistent with both Department policy and sound LDC practice in providing the most

beneficial service to its customers. The primary goal of CMA’s planning process is to acquire and

manage all available resources in a manner that achieves a best-cost resource portfolio for its

customers. A best-cost portfolio appropriately balances lower costs with other important non-cost

criteria such as reliability, viability and flexibility. Pursuit of a best-cost portfolio allows CMA to

provide its customers with reliable service at a reasonable cost.

The Company’s overall portfolio objective is supported by a number of specific resource

planning objectives, which are summarized as follows:

(1) minimize portfolio costs;

(2) maintain portfolio security/reliability (which includes enhancing diversity

across pipelines and supply basins);

(3) provide contract flexibility; and

(4) acquire viable resources.

CMA’s resource planning process employs analytical tools, including the SENDOUT®

cost optimization model along with its various assessment methods, to perform long-range

planning and to evaluate the individual resource decisions it must make. Non-cost resource

evaluation is typically performed using spreadsheet-based assessment tools. Taken together, these

tools and methods ensure that the planning process is thorough, and that it remains objective in its

pursuit of a best-cost portfolio.

2. CMA’s Planning Process

Effective resource planning requires both an excellent understanding of an LDC’s own

customers and markets, as well as insights into opportunities and developments in wholesale

markets. Through its resource planning process, CMA seeks to match its long-term resource needs

with available market opportunities (e.g., new capacity or gas supply options).


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CMA performs long and short-range analyses of its portfolio and potential need for

adjustment to achieve its planning objectives on an ongoing basis. Additionally, the Company

performs comprehensive analyses any time a decision to modify the portfolio of resources under

contract is being considered. This analysis includes a determination of need, and any associated

change of need, and an evaluation of potential resource options.

Any decision to modify the portfolio begins with a determination of need based on the

current resources under contract, including market (pricing) dynamics, and current demand

forecasts. CMA’s portfolio requirements are driven by CMA’s design weather conditions and the

associated requirements of its customers as reflected in its forecasts of (normal and design) annual,

peak season, cold snap and daily requirements developed using the forecasting models described

earlier in Section III. Comparison of the demand forecasts to the existing portfolio establishes

whether CMA’s portfolio is projected to be adequate over the planning horizon, and if not, the

quantity and duration of any deficiency. Similarly, this comparison also indicates whether there

is an imbalance of resources in the portfolio in any of the years over the planning horizon, which

may be released, de-contracted or sold in wholesale markets.

At the time that a need is established by a projected deficiency, CMA compiles a

comprehensive set of alternative portfolio options that could meet the anticipated need. CMA is

an active participant in regional capacity markets for both the purchase and sale of capacity

resources on a bundled and unbundled basis. CMA’s market participation provides important

market intelligence on developments in wholesale markets and is relied upon, in part, to compile

resource alternatives. Further, the Company may issue a Request for Proposal (“RFP”) as part of

the process to assure it receives the best bids from the market at that time. CMA also specifies the

criteria to be used in the evaluation of the array of resource options, which entails selecting the

appropriate weighting among the price and non-price evaluation criteria incorporated in the

planning process. Consistent with its portfolio goals, the resource evaluation criteria employed by

CMA are (1) price, (2) supply security, (3) contract flexibility and (4) supplier viability, which

take on varying degrees of importance depending on the type of resource decision being made and

anticipated market conditions.

All portfolio alternatives are scored using a consistent grading approach on a scale of 100

total points. The price of each resource is evaluated using the SENDOUT® model with a maximum

score typically equal to 30 points. Supply security is scored according to two separate components


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related to reliability and portfolio diversity usually set at maximums of 30 and 5 points,

respectively. Contract flexibility is scored according to the alternative’s nomination flexibility,

any minimum take requirements, ability to access storage, etc., and is typically assigned a

maximum of 20 points. Lastly, supplier viability is scored according to the financial integrity of

the entity and is usually awarded a maximum of 15 points. These criteria and assigned weights

have been approved in several prior F&SPs, including D.P.U. 08-79, D.P.U. 11-89, D.P.U. 13-

161, D.P.U. 15-143 and D.P.U. 17-166.

Once the full range of resource options has been analyzed, CMA selects the best resource

alternative or alternatives to pursue. In selecting the best alternative, CMA evaluates present and

anticipated future market conditions as well as risks associated with its decision. Depending on

the type of resource, there can be a long lead-time between the decision point and the in-service

date. This typically occurs when incremental capacity resources are required, which would be

taken into consideration in the Company’s Action Plan.

3. CMA’s Decision-Making Process Employs Least-Cost Planning Techniques

The first element of the Department’s standard of review is whether least-cost planning

techniques were used in the decision-making process. The Department has previously indicated

that CMA’s planning process appropriately minimizes costs:

The Company has provided evidence that it has a resource-planning process that ensures its ability to acquire least-cost supplies. With the use of the SENDOUT® model, Bay State is able to consider physical limitations and contract constraints, and to determine the minimum cost dispatch for a particular period (Exh. CMA-1, at 94-95).

The Department has held that, for a gas company’s planning process to minimize cost, that process must adequately consider all resource options, including energy efficiency, on an equal basis. D.P.U. 93-13, at 88. The evidence shows that the Company’s process adequately considers all resource options on an equal basis, and has appropriately accounted for the effect of its energy efficiency programs by subtracting from its demand forecasts the reduction in demand realized by such programs (Exh. CMA-1, at 98). Accordingly, the Department finds that the Company has formulated an appropriate process for identifying a comprehensive array of supply options, and has developed appropriate criteria for screening and comparing supply resources.

D.P.U. 17-166, at 46.


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In Bay State Gas Company, D.T.E. 98-86 (2000) (“D.T.E. 98-86”) and other Company

proceedings, the Department commented on CMA’s use of the SENDOUT® cost optimization

model to evaluate the cost effectiveness of various supply options, wherein it stated:

Bay State has demonstrated that it has in place processes by which it develops resource planning strategies to maintain reliable, least-cost service to its firm sales customers. The Department therefore finds that Bay State’s SENDOUT model allows the Company to identify a variety of capacity and commodity options under multiple planning contingencies and migration scenarios.

D.T.E. 98-86, at 30. CMA continues to utilize the SENDOUT® model as its primary tool for designing a least-

cost portfolio of supply options.

As explained more fully below, the Company also utilizes an appropriate analytical

framework for evaluating the cost-effectiveness of potential EE resources. Thus, CMA’s resource

planning process accomplishes the Department’s goal of achieving least-cost.

4. Analytical Tools

CMA utilizes important analytical tools to ensure a comprehensive evaluation of its total

portfolio resource alternatives and resultant decisions. Central among these is the use of CMA’s

SENDOUT® model that optimizes the utilization of all resources in the portfolio under various

weather patterns, including design and normal conditions. CMA also considers various growth

scenarios related to its design day and annual demand forecasts, including base, high and low.

This helps ensure that CMA’s planning techniques result in best-cost decisions. As noted above,

the Company’s use of this model has been cited by the Department in recent F&SP and other

proceedings as appropriate to ensure that CMA’s planning techniques are least-cost. SENDOUT®

can also select the lowest cost mix of resources from among an array of specified options. CMA

employs other analytical techniques, such as the use of spreadsheets, to enhance the evaluation of

resource options. These tools aid in the assessment of non-price criteria when there are a number

of similar options available in the marketplace.

CMA, through its collaborative participation in state-wide energy efficiency initiatives,

also employs appropriate analytical tools to evaluate demand-side resource options. In particular,

the Company employs a cost-effectiveness screening model developed through a collaborative

process. The evaluation of demand-side resources is based on an assessment of avoided energy


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costs to ensure that supply and demand-side resources are evaluated consistently to yield an overall

least-cost resource plan.

B. DESCRIPTION OF THE CURRENT RESOURCE PORTFOLIO

1. Overview of Supply-Side Resources

CMA’s upstream supply and capacity portfolio is comprised of a multitude of supply,

transportation, and storage contracts. These contracts are grouped into upstream capacity resource

paths, which flow gas from the supply source to the Company’s city gates. CMA’s upstream firm

capacity paths are listed in Appendix 2 and show all of the Company’s firm transportation, storage,

and supply resources. CMA’s long-term contracts are listed in Appendix 3, Table G-24.

Although CMA has three separate service divisions, for planning purposes, the Brockton

Division is separated from the Springfield and Lawrence Divisions because it is primarily served

by Algonquin Gas Transmission, LLC (“AGT” or “Algonquin”), and the Springfield and

Lawrence Divisions are primarily served by Tennessee Gas Pipeline (“TGP” or “Tennessee”). The

ability to transfer supplies between divisions is limited, with the only capability being the transfer

of up to 6,000 Dth per day to Brockton via a physical interconnect from Tennessee at the Mendon,

Massachusetts gate station. Also, CMA is able to exchange additional volumes on an as-needed

basis with Northern Utilities, Inc. (“Northern”), which allows the Brockton, Lawrence and

Springfield Divisions to receive in total approximately 12,000 Dth of supply when needed.

CMA’s supply-side resources are grouped into three categories: supply, storage, and

peaking. Supply and storage resources are delivered by transportation contracts held on various

upstream pipelines. Each group is discussed in greater detail below.

a. Supply Resources

CMA acquires firm supply through a combination of term and spot purchases. The

majority of CMA’s firm gas supply purchases are made pursuant to winter-only contracts that

deliver supplies from the U.S. Gulf Coast and other producing areas, including Marcellus Shale

(“Marcellus”), located on the Texas Eastern and Tennessee pipelines as well as supplies delivered

from the Dawn Hub into the TransCanada pipeline systems. In the summer, the Texas Eastern and

Tennessee pipelines are used primarily for transporting supply to storage facilities. Purchases for

storage refill are normally made on a spot basis or by utilizing asset management agreements for

ratable storage refill.


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For the most part, supply resources are purchased under the North American Energy

Standards Board (“NAESB”) gas supply contract. This NAESB contract includes several generic

provisions with the opportunity for parties to agree on different special provisions.

Regarding the structure for supply resource commodity purchases, LDCs can determine

the least-cost commodity resource purchase on a day-to-day basis by gathering market intelligence

via electronic trading platforms such as Intercontinental Exchange (“ICE”) as well as phone and

e-mail solicitations. In any case, CMA would only solicit and trade with those counterparties with

which the Company currently has an active NAESB base contract. Further, through the ICE

system, an LDC or other party may designate only those counterparties that it wishes to trade with

to be listed as available commodity sellers or buyers.

CMA’s portfolio diversity includes supply points from the U.S. Gulf Coast, Appalachia

and Canada.45 Consequently, CMA’s ability to purchase commodity on a daily basis, from diverse

locations, provides CMA’s customers with not only reliability, but the flexibility to adjust to

changing customer demand and market conditions.

b. Storage Resources

CMA currently has contracted for storage service from several upstream facilities. Some

provide service to the Brockton Division off the Algonquin system, while others serve the

Springfield and Lawrence Divisions off the Tennessee system. One storage service is capable of

serving all three Divisions.

For the Brockton Division, CMA has storage service contracts with Enbridge, Texas

Eastern and Dominion Transmission. These storage facilities are located near Dawn, Ontario and

in western Pennsylvania and western New York.

For the Springfield and Lawrence Divisions, CMA has storage service contracts with

Tennessee, National Fuel and Enbridge. The Tennessee and National Fuel facilities are located in

western Pennsylvania, and the Enbridge facilities are located near Dawn, Ontario.

45 The Company has contracts with receipts at Niagara and has entered into several agreements with TGP, PNGTS,

TCPL and Repsol to increase city gate capacity and provide reliable supplies from the Dawn Hub and the Canaport LNG import terminal.


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c. Peaking Resources

On-system peaking resources are those that CMA controls within its service territory and

are comprised of LNG and propane facilities located in each service territory. These on-system

resources are listed in Appendix 3, Table G-14.

CMA has contracts for off-system peaking supply, such as from Constellation Energy

(owner of the Distrigas LNG facility) and Repsol, which stores LNG, which can be vaporized and

delivered into the New England markets. Decisions to procure seasonal peaking supplies are

driven by an evaluation of design winter deficiencies that may be identified within the portfolio.

2. Changes to CMA’s Resource Portfolio from the Previous F&SP

Since CMA’s previous F&SP, a number of resources have been evaluated for renewal,

termination, replacement, addition or reduction. In the previous F&SP, the Company notified the

Department it would be required to make a decision regarding some of these resources. As a result

of its evaluations, CMA has renewed or terminated some of these resources. A brief discussion of

the resources that have changed since the prior F&SP is provided below.

Tennessee Contracts:

Contract No. 39741 for up to 4,081 Dth/day and Contract No. 5291 for up to 6,171 Dth/day were extended for five years and will now expire on March 31, 2025. Contract No. 5293 for up to 12,547 Dth/day was extended for 5 years and will now expire on October 31, 2024. Contract No. 5173 for up to 12,748 Dth/day and Contract No. 5178 for up to 19,755 Dth/day were extended for 5 years and will now expire on October 31, 2023. The Company entered into a new contract, No. 330904 with capacity additions scheduled over a three-year period: 50,000 Dth/day effective November 1, 2018; an additional 6,000 Dth/day beginning November 1, 2019; and the final 40,400 Dth/day increment on November 1, 2020. This contract has a primary termination date of October 31, 2038. The extension of these contracts and addition of contract No. 330904 is required to meet the Company’s on-going firm customer demand and is part of a best cost portfolio approach.

Algonquin Contracts:

Contract No. 510066 for up to 20,000 Dth/day was extended five years and will now expire on November 30, 2023. Through a capacity release auction process the Company added Contract Nos. 79990, 79991, 79992, and 79993 each with varying volumes of up to 5,000 Dth/day, and totaling 10,000 Dth/day, in October 2019 for a one year term. The new capacity primary firm delivery rights on Algonquin Transmission Pipeline (“AGT”)’s G lateral will increase the Company’s ability to deliver gas to Taunton or South Attleboro. As part of this capacity release auction process the Company has the option to retain this


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capacity beyond the initial one-year term and will be seeking Department authorization to exercise this option.

PNGTS Contracts:

The Company’s two former firm transportation service contracts with PNGTS expired March 9, 2019 (Contract Nos. 1997-001 and 1997-002). The Company entered into contract No. 208535 for 45,500 Dth/day to replace the expired contracts. This new contract will expire on October 31, 2040. Additionally, the Company entered into a Precedent Agreement under PNGTS’ Portland Express Project (“PXP”), approved by the Department in DPU 17-172, for an additional 14,300 Dth/day of PNGTS capacity which will become effective on November 1, 2019 (12,675 Dth/day) and November 1, 2020 (1,625 Dth/day). This new contract will also expire on October 31, 2040. Included in the agreements for acquisition of new capacity are provisions for the acquisition of capacity on Transcanada Pipeline Company (“TCPL”) and Union Gas Limited (“Union”) that will provide for a seamless transportation path to the growing supplies at the liquid Dawn Hub located in Dawn, Ontario. Dominion Storage Contracts:

The Company extended Dominion Storage contract No. 600002 for up to 14,758 Dth/day for five years and the contract will now expire on March 31, 2026.

Enbridge Storage Contracts:

Contract No. UTEC-CMA-3 for up to 16,000 Dth/day of Enbridge storage in the Dawn Ontario region went into service on April 1, 2018, and will expire on March 31, 2022. Enbridge contract No. LST089 for up to 26,500 Dth/day is in service and will expire on March 31, 2022. Granite Contract:

The Company entered into contract No. 22-002-FT-1 for up to 12,000 Dth/day, which will expire on October 31, 2020. The new contract is effectively an extension of the previous contract No. 21-001-FT-1. CMA has a year-to-year physical exchange agreement with Northern Utilities. Through this exchange agreement, Northern Utilities delivers 12,000 Dth/day directly to the CMA city gate. In exchange, CMA delivers 12,000 Dth/day to the Northern Utilities city gate using this Granite State Gas Transmission (“GSGT”) FT-1 capacity. CMA utilizes flowing supply from PNGTS to fill this GSGT capacity. Millennium Contract:

CMA has entered into contract No. 217524 for 15,000 Dth/day which will expire on March 31, 2034. This contract is part of a path that delivers purchased supply to CMA city gate, while also allowing CMA to directly access Marcellus supply.


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Transco Contract:

Contract No. 1006548 for up to 1,254 Dth/day of FT legacy capacity has been extended for one year and now expires March 31, 2020. This capacity provides for the delivery of storage to CMA’s city gates. It is required to meet the Company’s on-going firm customer demand and is part of a best-cost portfolio approach. Vector Contracts:

The Company has allowed all Vector contracts to expire as planned. On-System Peaking:

There have not been any changes to CMA’s On-System Peaking assets since the prior F&SP. CMA will continue to evaluate the operational capabilities of these and other on-system peaking facilities annually as each facility continues to age and the operational requirements of the Company’s distribution system continue to change. Should the results of ongoing analyses suggest a change in the daily and/or seasonal capability of any of CMA’s peaking facilities, CMA will provide the updated capabilities within the F&SP process or other appropriate filing with the Department. Peaking Supply:

CMA has two transaction confirmations for LNG supplies from Repsol’s Canaport LNG terminal for delivery on Tennessee capacity. A 30-day peaking supply for up to 32,900 Dth/day and a 40-day peak supply for up to 14,100 Dth/day are both contracted through March 31, 2028. Additionally, the Company has entered into a contract with Constellation Energy which provides for the firm delivery of up to 8,000 Dth/day to Company receipt points on AGT’s Line G for the 2019-20 winter season.

3. Demand Side/Energy Efficiency Resources

CMA offers comprehensive Energy Efficiency (“EE”) services aimed at reducing customer

demand. All current EE programs through December 31, 2021, as described in the Company’s

three-year EE plan for 2019-2021, were approved by the Department on January 29, 2019, in

D.P.U. 18-110.46 Each three-year plan is developed, including an annual therm savings goal, with

the guidance of the Commonwealth’s Energy Efficiency Advisory Council (“EEAC”) and in close

collaboration with the other gas and electric LDCs. CMA’s EE programs are tested for cost-

effectiveness on a total-resource cost basis.

46 As explained earlier, the savings goals in the 2019-2021 EE Plan represent a 70% increase over the Company’s goals in its 2016-2018 EE Plan. Given the magnitude of this increase over historical achievement and prior savings goals, CMA has relied on the historical actual energy savings from EE programs to estimate forecasted levels of EE savings in this F&SP. The Company will review its full year 2019 achieved savings against 2019 savings goals and make a supplemental filing in January 2020 based upon its review.


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CMA offers EE services to all customers: residential, residential low-income, and

commercial and industrial (“C&I”). CMA’s EE programs have been designed and implemented

in coordination with other Massachusetts natural gas LDCs and electric LDCs, and are consistent

with the program offered by other Massachusetts natural gas LDCs. The core residential program

is Residential Coordinated Delivery, which provides a no-cost home energy efficiency assessment

to residential properties. This program provides customers with seventy-five percent of the cost

of insulation improvements, no-cost air sealing, and zero percent financing. In addition, CMA

offers rebates for heating and water heating equipment. CMA also has an Income Eligible program

offering all the same benefits and services to customers who are eligible for fuel assistance or the

utility discount rate at no cost to the customer.

Other program offerings benefiting residential customers include a Residential New

Buildings program that provides incentives to builders of new homes exceeding local building and

energy codes, thereby promoting market acceptance of high efficiency design and increasing the

penetration of highly efficient new homes. The program also similarly incentivizes customers

performing major renovations or additions.

C&I customers are eligible for broad array of energy efficiency services. The New

Construction and Major Renovation program offers a range of services, including design and

engineering assistance, as well as incentives for the purchase of high efficiency HVAC systems,

measures that improve the building envelope, and other major equipment to the developers of new

buildings and the owners of buildings undergoing major renovations. The C&I Existing Buildings

program encourages building owners to replace functioning equipment with premium efficiency

counterparts. Customers are offered incentives for the purchase and installation of HVAC

equipment and controls, building energy management system controls, industrial process

equipment and controls improvements, spray valves and other equipment specific to a customer’s

needs. Therefore, in conducting its resource-planning process, the Company carefully considers

the actual, cumulative demand reduction resulting from energy efficiency programs.

C. ANALYSES UTILIZING SENDOUT®

In order to assess the cost implications of various resource alternatives, CMA performs

optimization analyses using SENDOUT®. CMA augments these cost analyses with assessment

of non-cost characteristics in order to support its various resource decisions. Also, SENDOUT®


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is used to assess the adequacy of the resource portfolio under different levels of firm customer

requirements.

This section of CMA’s Plan presents current SENDOUT® results based on its long-range

forecast of requirements, existing resources and potential new supply resources. The results of

these and recent analyses of the cost-effectiveness of potential EE measures form the basis of the

Company’s present Action Plan.

The SENDOUT® model is a linear programming software package designed for LDCs to

optimize the cost of serving demand while ensuring reliable service to firm customers.

Specifically, SENDOUT® incorporates the monthly demand forecast, converts this forecast into

a daily interval, and then satisfies daily demand by utilizing the lowest cost resources from among

those specified in the available network. CMA’s model includes limitations on the withdrawal of

storage and peaking facilities to ensure that these assets are available throughout the heating season

as one way to ensure reliable service to firm customers.

SENDOUT® assumes that all demand costs are fixed and all supplies are optimized based

on variable costs. However, SENDOUT® can evaluate certain selected resources on a total cost

basis. This evaluation is referred to as the Resource Mix option, and can be used to test whether a

new contract should be entered into or whether an existing contract should be renewed. The

Resource Mix option can also “size” a contract when given a maximum and minimum range from

which to select. SENDOUT® is capable of handling several supply, transportation, and storage

resources placed into the Resource Mix at one time.

CMA utilizes SENDOUT® to test the adequacy of its resource portfolio, including any

required incremental resources, under various design conditions. As described earlier, CMA’s

design conditions include design day, design winter and cold-snap weather conditions.

CMA’s analyses, under a variety of demand scenarios, indicate that the portfolio is

sufficient to satisfy CMA’s base case firm demand without the acquisition of incremental resources

during the forecast period. The adequacy of the portfolio is due, in part, to the growth of CMA’s

firm requirements under its base case scenario. This level of growth is a function of the projection

of declining NUPC, somewhat dampening the load growth associated with the impact of projected

customer growth


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It is important to note that the adequacy of the Company’s portfolio to satisfy firm demand

under the base and high case scenarios throughout the five-year forecast period (through October

2024), is in part due to CMA’s plan to acquire capacity to attain this portfolio adequacy.

Detailed results showing the resources utilized to meet firm customer requirements under

each of the demand growth scenarios are provided in accompanying appendices. Appendix 3,

Table G-22N, provides the winter and summer dispatch results for the base, and high demand

growth cases based on normal year requirements. Incremental Resources are required to meet

increasing customer demand for each demand scenario, as shown in Table IV-1.

Table IV-1: Incremental Resources by Scenario (Dth per Year)

Scenario 2019-20 2020-21 2021-22 2022-23 2023-24 Base Case –

Normal 0 0 0 0 0

Base Case - Design

0 0 2,369 4,104 5,948

High Case – Normal

0 0 0 0 0

High Case - Design

2,170 6,925 12,075 22,050 31,889

Base Case – Cold Snap

0 2,671 5,599 7,895 12,356

High Case – Cold Snap

5,701 27,746 40,466 52,294 63,749

Incremental Resources are not required in a Normal Year in either the Base Case or High

Case Growth Scenarios. However, in a Design Year, regardless of growth, and in the Cold Snap

Scenarios Incremental Resources in small amounts are required in most years of the plan. These

resources are not significant and the Company would expect to cover any Incremental Resource

needed with City Gate Supply, or a pipeline expansion should one become available and was

selected by the Company’s planning process as best cost option and also approved in a separate by

the Department.

Appendix 3, Table G-22D, provides the winter and summer dispatch results for the high

growth and base case based on design winter requirements. Incremental Resources are required

in the high demand growth case under design conditions, up to approximately 31,889 Dth/Y.

Should this condition occur, CMA would anticipate contracting for city gate peaking supply and


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spot purchases to serve the incremental requirements. Appendix 3, Table G-22CS, provides the

winter and summer dispatch results for the Cold Snap scenarios, showing 12,356 to 63,749 Dth/Y

of incremental resources would be required to serve firm demand under this condition during Base

and High Growth Scenarios. Lastly, Appendix 3, Table G-23, provides the dispatch results on a

design day for the base, and high growth cases. As stated above, all of these scenarios reflect the

renewal of existing capacity resources during the five-year period ending October 31, 2024.

Overall, CMA is presently projecting a modest resource deficiency in the base case

scenario, which will be served through a combination of peaking supplies, via an RFP process,

and day-to-day spot supply. Also, as noted above, CMA will be required to evaluate a number of

important contract renewal decisions during the five-year planning horizon. These include

capacity on Tennessee, Algonquin, Iroquois, Millennium, National Fuel, DTI, Transco,

TransCanada, Union, PNGTS, and TETCO. Appendix 3, Table G-24, highlights all contracts that

terminate during the forecast period, terms and required notice dates for renewal, as well as

indicating those contracts having an evergreen provision. Some of these contracts provide

important primary delivery point capacity needed to maintain the reliability of CMA’s system.

As the decision time nears for each of these renewal decisions, CMA will employ its

resource planning process to establish the best-cost alternative, which may be renewal,

replacement, reduction or termination of all of the existing resources, as explained in Section I,

above. The Department will be notified of any long-term renewal decisions and, further, any new

long-term capacity contracts, as may be required, will be filed with the Department, along with the

appropriate support, for approval under G. L. c. 164, § 94A. In this F&SP, the Company requests

specific approval to renew all contracts with renewal notice required within the two-year period

from the filing of this document and also all contracts just outside of the two-year window with

renewal dates prior to October 31, 2021. These contracts are noted on Table G-24.

D. EVALUATION OF DEMAND-SIDE RESOURCES

CMA considers both supply and demand-side options on an equal footing. The evaluation

of demand-side resources on a consistent basis with supply-side resources is accomplished through

a separate screening process utilizing appropriate analytical tools. Avoided energy supply costs

are the basis for determining the cost-effectiveness of alternative demand-side resources. In

Massachusetts, the supply-side avoided costs utilized by all LDCs in their EE Plans are prepared


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on a regional basis and are updated biannually. The most recent regional avoided cost study is the

Avoided Energy Supply Costs in New England: 2018 Report (“AESC 2018”), which was

completed on March 30, 2018.

An EE program cost-effectiveness screening model is utilized to evaluate EE resources.

The model incorporates an array of descriptive parameters, in addition to the avoided energy costs,

to calculate the expected lifetime energy savings of EE measures. Screening is performed on a

total-resource cost test basis as currently specified by the Department. The EE program is

discussed further in Section II.

E. NON-COST ANALYSES

In addition to a cost analysis, CMA evaluates other attributes of potential resources,

including reliability, flexibility and viability. This non-cost evaluation is accomplished through

appropriate assessment techniques and scoring, and is integrated with cost-considerations in order

to arrive at final resource decisions. CMA will present a comprehensive analysis of both cost and

non-cost considerations associated with available alternatives at the time the Company requests

Department approval of any specific long-term resource option.

F. OTHER INFORMATION

As reported in its last F&SP, the Company has none of the following: (a) any participation

in or service from manufacturing and storage facilities planned outside Massachusetts; (b) an

exempt and approved manufacturing or storage facility in Massachusetts not yet in operation; (c) a

proposed manufacturing or storage facility in Massachusetts; or (d) a proposed pipeline in

Massachusetts over a mile in length and over 100 psi.

G. OPERATIONAL CONSIDERATIONS

Although CMA’s F&SP is a comprehensive plan intended to reliably service the long term

demands of its customers, CMA nonetheless faces operational risks in the day-to-day management

of its system. Some of these risks are inherent and quantifiable, such as the risk that the weather

could be colder than design day or extends longer in duration than CMA’s planned cold snap.

Other risks, however, are outside of CMA’s direct control. For example, a few years ago, TGP

installed an electric compressor to support the Northampton lateral. A failure of this singular

compressor station due to electrical interruption or mechanical failure could lead to significant


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pressure drops on the Northampton lateral and potentially result in disruption of service to CMA’s

customers.

Electric generation facilities are now the largest consumers of natural gas, larger than the

natural gas LDC community. Electric generation facilities tend to burn their daily allotment of gas

in a very short period of time, typically in less than a twelve-hour period. The increasing utilization

of the natural gas system by electric generating facilities, when the natural gas system was designed

for natural gas LDC usage, which is more consistent throughout the day, has resulted at times in

very low instantaneous pipeline pressures. This threatens the overall viability of the natural gas

system.

The increased demands resulting from (1) new electric loads attaching to the pipeline grid

without corresponding pipeline capacity and (2) new customers converting to natural gas have

resulted in the natural gas pipeline grid running consistently at or near design peak day levels.

Several years ago, when there was more flexibility of available capacity in the pipeline grid, TGP

would restrict their pipeline at station 245, the entry point into Massachusetts (Zone 6). Now, due

to the increased electric load, changing supply dynamics, and increased overall demand, TGP has

begun to restrict their pipeline through every existing compressor station in Massachusetts,

something never experienced before. CMA’s planning standards have not traditionally included a

gate by gate specific supply/demand balance. CMA’s customers do not take supply on an evenly

hourly basis nor does CMA plan for evenly hourly take restrictions, which the pipeline can

institute.

Additionally, when the pipeline runs consistently at or near its design day capabilities, there

is a much greater risk that the pipeline grid could experience a widespread interruption of service,

due to the fact that the pipeline compressor stations are running at high utilization rates, and are

therefore more prone to breakdown. When compressor stations breakdown, this causes overall

lower system pressures and throughput. This may result in the pipeline cutting firm pipeline

capacity, resulting in CMA potentially using much more peaking resources than planned. When

a compressor fails, it has the immediate effect of lowering pressures downstream of that

compressor station and the pipeline is forced to cut flowing gas through that point. The net result

is that CMA must strive to maintain higher levels of on-system peaking resources in the event of

pipeline curtailments.


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H. SPRINGFIELD DIVISION – RELIABILITY PLAN

The prior F&SP discussed a reliability plan for Springfield in detail. The reliability plan

at that time included five projects to accomplish the goal of enhancing the reliable, safe and

continuous delivery of natural gas service to approximately 110,000 customers in 16 municipalities

in the Springfield operating area. The plan now consists of four integrated projects.

These separate projects are designed to meet the challenges for uninterrupted and reliable

service to customers. The projects include: (1) the Longmeadow Supply Strategy Project, which

is a new Point of Delivery to be installed by Tennessee in the Town of Longmeadow that will

enhance system reliability for customers on both sides of the Connecticut River, and offer

economic growth opportunities through enhanced gas supply availability;47 (2) the Agawam

Compressor Station Enhancement project, in which TGP will upgrade equipment at its existing

compressor station to improve operating efficiency and deliver enhanced services to Columbia

Gas that will increase reliability to its customers; (3) the TGP Agawam 2 Mile Pipeline Loop will

provide additional capacity and operational pressure that ensures reliable service on the western

end of the Springfield operating area; and (4) the Columbia Gas ConEd Transmission Line

Replacement, which will replace an 8,500 foot existing line with new pipe in Springfield to

increase reliability and improve system flexibility. Columbia Gas will conduct customer outreach

and listening sessions, and will engage with the community in an open and transparent manner

throughout each project process.

The Company has obtained Department approval of a contract entered into with the TGP

for the first three projects. This contract allows for 96,400 Dth of firm transportation capacity,

with increased delivery pressure of 300 psi at its Agawam point of receipt, an increase in delivery

pressure to 200 psi at its Lawrence point of receipt and an additional point of delivery from TGP.

This capacity will be fully available by November 1, 2020.

Since the last F&SP and after further analysis, the Company has decided to cancel one

proposed project—the Alternate Backfeed—which consisted of six miles of 12" pipe to allow

additional gas supply to Northampton and Easthampton. As a result, the moratorium on natural

gas service in Northampton and Easthampton will remain in place.

47 Approval from the Energy Facilities Siting Board for over three miles of new distribution piping will be necessary.


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I. TAUNTON & ATTLEBORO – AGT LINE G ISSUE

The Company serves its Taunton and Attleboro areas (part of the Brockton Division) with

capacity on AGT. The AGT lateral that serves these areas is AGT’s Line G. For decades, the

Company has used various AGT contracts to serve Taunton and Attleboro. Historically, CMA’s

supply nominations have been made to an Allocation Point and AGT managed all city gates takes

in total against nominations to CMA’s Algonquin served market.

CMA has adequate capacity levels on AGT coupled with on-system peaking to meet

projected total base case design day demand of its Brockton Division planning load customers for

the next two years. However, recently, AGT indicated that it may need to impose Line G specific

operational flow orders, including the possibility of requiring 24-hour ratable deliveries or

deliveries to a specific point(s). The Company’s contracts and system were not designed for such

targeted pipeline operations. AGT’s actions place the Company at risk of incurring penalties for

over taking its contractual entitlements at Taunton and Attleboro, which has never before been an

issue. Therefore, the Company is working with the pipeline and other LDCs to resolve this issue

and to procure other resources to address potential operational flow orders.

Although AGT has no unsubscribed firm capacity on Line G, the Company has acquired

two additional resources, as shown in this F&SP, to help mitigate the risk to the Company’s

customers in Taunton and Attleboro. These resources are: (a) pipeline capacity release agreements

from Engie; and (b) a firm peaking agreement with Constellation Energy for the upcoming winter

period. The Company has the ability to extend some of these arrangements for multiple years and

plans to file for Department approval of these longer-term contracts. These resources, coupled

with the Company’s existing portfolio, will help ensure adequate supply and capacity for the

Brockton Division in total and will reduce the risk of potentially higher costs imposed by

overtaking supplies at Taunton and Attleboro while under a Line G specific operational flow order.

In addition to the above-described efforts, the Company continues to pursue additional

supply resources for its Taunton and Attleboro area markets. First, the Company is reviewing

adding temporary CNG and/or LNG supplies deliverable to its distribution system, including

possible locations and associated permitting requirements and logistic needs. The Company has

also engaged in discussions with Eversource and National Grid about the potential for providing

the Company with a supply service(s) associated with the management of on-system resources

controlled by the respective companies.


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V. CMA’S ACTION PLAN

Based on this F&SP, CMA faces a number of important supply-side resource decisions

with respect to existing capacity contracts in order to continue its obligation to provide safe,

reliable and least-cost service to its customers. As part of this process, the Company maintains a

current list of all of its capacity contracts and the associated expiration dates. In the weeks and

months leading up to a required renewal decision, the Company undergoes an analysis as to

whether or not renewal of these capacity contracts is warranted. Sometimes, this analysis can be

complex and yet other times the decisions can be rather straightforward, such as the renewal of

transportation capacity, which services constrained areas of CMA’s distribution system and

continues to achieve a least-cost portfolio.

The Company expects to take advantage of roll-over rights for the majority, if not all, of

its capacity contracts over the forecast period, thus maintaining the capacity the Company requires

to provide reliable service to meet expected customer demands in the aggregate in all growth

scenarios and weather conditions considered in this F&SP. CMA will continue to closely monitor

customer requirements so that it can take the necessary actions to ensure reliability if actual usage

levels trend closer to the Company’s forecast of high growth requirements. Further, as decision

time nears for each of these contract decisions, the Company will determine the range of

alternatives available in the marketplace, if any, and will employ its resource planning process to

establish the least-cost alternative, which may be renewal or replacement of some or all of the

existing resources.

The Company requests specific approval of the following contracts that have renewal

notification dates that occur within two years of this filing:

Tennessee Transportation and Storage Capacity Renewals: The Company’s Springfield and Lawrence Divisions receive all of their supplies via Tennessee transportation capacity. This capacity is largely legacy long haul and short haul transportation capacity and market area storage capacity. The Tennessee capacity provides a competitively-priced service offering and important supply diversity benefits to the portfolio. At this time, CMA intends to renew all of its firm transportation and storage capacity contracts on the Tennessee system, which contain a right of first refusal provision. Within the first two-year period of this F&SP, the Company plans, and requests approval through this F&SP, to renew the following contracts: 330904, 48426, 48427, 41098, and 95349. Note that contract 48426 and 48427 will be reduced to 8,000 Dth/Day.


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Algonquin Transportation Capacity Contract Renewals: The Company’s Brockton Division customers receive all of their supply from Algonquin legacy capacity via interconnects with upstream pipelines including TETCO, Tennessee, Millennium and Maritimes and Northeast Pipeline. At this time, CMA plans to renew all firm transportation capacity contracts on the Algonquin system. Within the first two-year period of this F&SP, the Company plans, and requests approval through this F&SP, to renew the following contracts: 94501, 93201AC, 93401, 93001EC, 93001F, 799990, 799991, 799992, and 799993. National Fuel Transportation and Storage Capacity Renewal: The Company has a storage and transportation contract with National Fuel that provides delivery of underground storage supplies to Tennessee for transport to the Company’s Springfield and Lawrence Divisions. These legacy capacity contracts provide much needed balancing flexibility and supply reliability for CMA’s Springfield and Lawrence customers. At this time, CMA intends to renew these legacy transportation and storage capacity contracts. Within the first two-year period of this F&SP, the Company plans, and requests through this F&SP, approval to renew the following contracts: N11117 and O11116.

Transco Short-haul Contract Renewal: CMA has a transportation contract with Transco that provides firm downstream capacity from DTI’s GSS storage facility located in Pennsylvania. This contract provides much needed supply reliability for CMA’s Brockton customers. At this time, CMA intends to renew this legacy transportation capacity contract. Within the first two-year period of this F&SP, the Company plans, and requests through this F&SP, approval to renew the contract 1006548. Union Gas Transportation Capacity Renewal: The Company is seeking approval to renew contract M12204. Granite Transportation Capacity Renewal: The Company is seeking approval to renew Granite contract 22-001-FT-1. Iroquois Transportation Capacity Renewal: The Company is seeking approval to renew Iroquois contract R182001. Texas Eastern Capacity Renewal: The Company has a storage contract, 400193 with Texas Eastern and is requesting renewal. Enbridge Storage Capacity Renewal: CMA has storage contracts with Enbridge. The company is requesting to renew contracts UTEC-CMA-3 and LST089.

As explained in Section III, the customer segment demand forecasts discussed above

include the effects of historical energy efficiency savings and expected future energy efficiency


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savings at historical levels. The forecast assumes that EE savings will continue to accumulate each

year at levels on par with actual historical EE savings achieved. However, the Company will

review its full-year 2019 achieved savings against 2019 savings goals and make a supplemental

filing in January 2020 based upon this review.

VI. CONCLUSIONS REGARDING CMA’S RESOURCE PLAN

The Company’s F&SP, planning process and results have been subject to Department

review in several previous filings pursuant to G.L. c. 164, § 69I. Also, requests for approval of

long-term contracts have been subject to Department review in several filings pursuant to G.L.

c. 164, § 94A. In this F&SP, the Company continues to utilize essentially the same planning

process as has been employed since the time of its most recently approved F&SP, D.P.U. 17-166.

CMA has demonstrated that this F&SP meets the Department’s standards for approval and

is reviewable, appropriate and reliable. With respect to the Company’s supply resource portfolio,

the Plan indicates that CMA’s resource portfolio is adequate to meet the projected base case

throughput requirements of its customers over the term of the forecast period, given the rollover

and renewal of key existing pipeline transportation and storage capacity contracts and the

acquisition of additional pipeline capacity. Further, CMA’s planning process achieves a least-cost

portfolio, where resource decisions appropriately balance cost considerations with those related to

the reliability and security of supply, contract flexibility and resource viability.

CMA will carry out the elements of its Action Plan consistent with any guidance or

direction from the Department. CMA will file for Department approval long-term contracts related

to specific resources in its portfolio that result from the application of the Company’s resource

planning process. CMA has complied with the Department’s order in D.P.U. 15-143 to request

approval to renew any contract that is due to expire within two years from October 31, 2019. CMA

has specifically identified contracts that it is requesting approval to renew in Appendix 2 G-24.

CMA will rely on the results of this Plan as a guide in completing future resource analyses.

BAY STATE GAS COMPANY d/b/a COLUMBIA GAS OF MASSACHUSETTS

2019 LONG RANGE FORECAST AND

SUPPLY PLAN 2019/2020 – 2023/2024

APPENDICES

October 30, 2019

COLUMBIA GAS OF MASSACHUSETTS 2019 Forecast and Supply Plan

APPENDICES

i

APPENDIX 1: CMA’S RESOURCE PLANNING PROCESS…………………………………….. A-1 APPENDIX 2: CMA’S EXISTING CAPACITY PATHS………………………………………….. A-2 APPENDIX 3: ENERGY FACILITIES SITING BOARD TABLES……………………………… A-3

Table III-2 Normal Yr Planning Load Forecast Result Summary……………………..A-3

Table DD: Effective Degree Day Data…………………………….….……………… A-5

Table FA: Forecast Accuracy………………………………………………………… A-8

Table G-1: Residential Heating Class………………………………………………… A-9

Table G-2: Residential Non-Heating Class……………………………….….……… A-13

Table G-3 (C&I LLF): Commercial and Industrial Class (Low Load Factor)……………… A-17

Table G-3 (C&I HLF): Commercial and Industrial Class (High Load Factor)…………… A-21

Table G-3 (CE): Capacity Exempt…………………………………………………………A-25

Table G-4(A): Interruptible…………………………………………………..…………. A-28

Table G-4(B): Sales for Resale, Firm…………………………………..………………. A-29

Table G-4(C): Company Use, Unbilled and Unaccounted For……………….………… A-30

Table G-5 (A): Total Firm Company Sendout………………………………………...…. A-34

Table G-5 (B): Total Firm Company Planning Load …………………………………….A-38

Table G-6: Impact of Causative Variables on Use Factors………………………..… A-42

Table G-14: Existing On-System Peaking Resources………………………………… A-43

Table G-15: Participation in or Services from Manufacturing and Storage Faculties Planned Outside of Massachusetts…………………………………….... .A-44

Table G-16: Exempt and Approved Manufacturing and Storage Facilities in MA and Not Yet in Operation……………………………………………………. A-44

Table G-17: Proposed Manufacturing and Storage Facilities in MA………………….A-44

Table G-21: Proposed Pipelines in MA Over a Mile in Length and Over 100 PSI…....A-44

Table G-22N: Requirements vs. Resources: Normal Year………………………………A-45

Table G-22D: Requirements vs. Resources: Design Year……………………………….A-49

Table G-22CS: Requirements vs. Resources: Cold Snap……………...………………….A-53

Table G-23DD: Requirements vs. Resources: Design Day………. ………………………A-57

Table G-24: Long-Term Contracts as of November 1, 2019……………… …….……A-59

COLUMBIA GAS OF MASSACHUSETTS 2019 Forecast and Supply Plan

APPENDICES (continued)

ii

APPENDIX 4: SUMMARY OF DEMAND FORECASTING FRAMEWORK…………………A-60 APPENDIX 5: FORECAST AND DEPENDENT VARIABLE GRAPHS ……………………….A-61

APPENDIX 6: CALCULATION OF BILLING CYCLE EDD VARIABLE………………….....A-67 APPENDIX 7: CALCULATION OF DESIGN DAY, PRIOR DAY AND DESIGN WINTER EDD………………………………………………………………………………………….………....A-68 APPENDIX 8: STATISTICAL TECHNIQUES AND GLOSSARY……………………………...A-71 APPENDIX 9: DETAILED MODEL STATISTICS – CUSTOMER SEGMENT MODELS …..A-78 APPENDIX 10: COMPANY USE………………………………………….……………………….A-346 APPENDIX 11: CALCULATION OF GAS PRICES…………..…..…………….……………….A-349 APPENDIX 12: ANNUAL SUMMARY OF RESULTS…………………………………………...A-352 APPENDIX 13: DAILY PLANNING LOAD……………………………………………….……...A-360 APPENDIX 14: DESIGN YEAR AND DESIGN DAY PLANNING LOAD RESULTS……..…A-366 APPENDIX 15: FORECAST CALCULATION AND DATA FLOW............................................A-373

Resource Evaluation

Forecast of Requirements

1

Data Collection

• Usage, customers, demand-side savings

• Weather EDDs• Economic series

A

Forecast Models

• Residential, Commercial

• Econometric• Base, high and low

scenarios

B

Planning Standards

• Normal weather• Design day, winter• Cold snap

D

Demand-Side Reductions

• Forecast of savings

C

2

Portfolio Goals

• Cost• Non-cost

A

Identify Need

• Comparison of requirements and existing resources

B

Non-cost Analysis

• Analysis of resource reliability, flexibility, diversity and viability

D

Cost Analysis

• Supply Portfolio simulation - normal, design conditions

• Energy Efficiency Screening

C

Resource Action Plan

3

Contracting Plans

• RFP process• Negotiation• Communication with

stakeholders as needed• Request Department

approval, if necessary

A

Decontracting Plans

• Negotiation with providers

• Notice

B

Appendix 1: CMA’s Resource Planning ProcessColumbia Gas of Massachusetts 2019 Forecast and Supply Plan

APPENDIX 1 Page 1 of 1

Page A-1

APPENDIX 2 Columbia Gas of Massachusetts

Existing Capacity Paths

SupplyPath # Segment # Contract Expiration Source

A 1 TGP/FT-A 31-Oct-23 Texas 4,462Louisiana 8,286

12,748 12,748 CMA CITYGATE

B 1 TENN Storage 31-Oct-23 NY / Penn 19,755 19,755 TGP ELLISBURG2 TGP/FT-A 30-Apr-45 15,375 CMA CITYGATE3 TGP/FT-A 31-Oct-24 4,171 CMA CITYGATE

C 1 NAT FUEL Storage 31-Mar-20 NY / Penn 10,000 10,000 Nat Fuel2 NAT FUEL FS-1 31-Mar-20 8,376 TGP ELLISBURG3 TGP/FT-A 31-Oct-24 8,376 CMA CITYGATE

D 1 TGP/FT-A 31-Mar-25 Niagara, NY 6,171 6,171 CMA CITYGATE

E 1 TGP/FT-A 31-Mar-25 Niagara, NY 4,081 4,081 CMA CITYGATE

F & G 1 ENBRIDGE STORAG 31-Mar-22 26,5002 UNION 31-Oct-22 Dawn 26,352 26,352 Parkway3 TRANSCANADA 31-Oct-26 26,063 TCPL

TCPL5 SPOT Waddington, NY6 IGTS/RST-1 01-Nov-22 28,840 28,840 IROQ WRIGHT7 TGP/FT-A 31-Oct-22 6,068 CMA CITYGATE F8 TGP/FT-A 31-Oct-22 3,706 CMA CITYGATE F8 TGP/FT-A 31-Oct-22 18,733 AGT MENDON9 AGT/FT-2 31-Oct-22 18,490 CMA CITYGATE G

I & S 1 TETCO/CDS 31-Oct-22 Texas 16,408Louisiana 37,687

36,369 36,369 AGT LAMBERTVILLETETCO

2 SPOT (S) Lambertville/Hanover 36,3692 AGT/AFT-E/1 31-Oct-19 5,4893 AGT/AFT-E/1 31-Oct-19 5,6904 AGT/AFT-E/1 31-Oct-19 27,757

38,936 CMA CITYGATE

J 1 TETCO Storage 30-Apr-23 NY / Penn / WV 22,819 22,819 AGT LAMBERTVILLE2 AGT/AFT-E/1 31-Oct-19 22,819 CMA CITYGATE

K 1 TETCO Storage 30-Apr-27 NY / Penn / WV 1,056 1,056 STOR W/D POINT2 TETCO CDS 31-Oct-27 1,056 AGT LAMBERTVILLE3 AGT/AFT-E/1 31-Oct-19 1,056 CMA CITYGATE

L 1 DOM Storage 31-Mar-26 NY / Penn / WV 14,758 14,758 STOR W/D POINT2 TETCO- FT 31-Oct-22 4,235 AGT LAMBERTVILLE2 TRANSCO/FT 31-Mar-20 1,254 AGT LAMBERTVILLE3 SECONDARY 9,269 CMA SECONDARY4 AGT/AFT-1 31-Oct-19 14,758 CMA CITYGATE

M 1 ENBRIDGE STORAG 31-Mar-22 Dawn 16,0002 TRANSCANADA 31-Oct-26 16,000 PNGTS Pittsburgh, NH3 PNGTS/WS 31-Oct-33 16,000 TGP Haverhill4 TGP FT 31-Oct-20 Effec. 11/01/2019 16,000 CMA CITYGATE

6B Repsol Peaking 31-Mar-28 Canaport 47,0007 TGP FT 31-Oct-20 Effec. 11/01/2019 34,000 TGP Dracut

22,000 TGP Haverhill8 47,000 CMA CITYGATE

N 1 UNION2 TRANSCANADA3 PNGTS/FT 31-Oct-20 Pittsburgh, NH 45,500 Haverhill4 GSGT FT 31-Oct-19 12,000 CMA CITYGATE

H 5 TGP FT 31-Oct-32 6100 CMA CITYGATEQ 6 TGP FT 31-Oct-20 17,000 CMA CITYGATE

O 8B UNION NEW 11-01-19 Dawn 12,6759 TRANSCANADA NEW 11-01-19 1267510 PNGTS/FT NEW 11-01-19 23,075 CMA CITYGATE

P 1 AGT AFT-1(H) 30-Nov-23 Beverly, MA 20,000 20,000 CMA CITYGATE

R 1 AGT AFT-1 (X-35) 10/31/2023 Transco 48,000 48,000 CMA CITYGATE

T 1 AGT (AFT-1) 10/31/2031 Ramapo, NJ 30,000 30,000 AGT Brockton/Sharon Station, MAAGT (AFT-1)H 10/31/2020 10,000 AGT Taunton/South Attleboro

30,000 CMA CITYGATE

Columbia Gas of Massachusetts 2019 Forecast and Supply Plan


Page A-2

Table III-2: Normal Year Planning Load Forecast Result Summary

Gas Year [1]Residential

DemandC&I

DemandCompany

Use

Firm Unbilled and

Losses

Total Firm After

Unbilled and Losses

Capacity Exempt


Losses

Total Planning

Load

2014-2015 13,945,110 12,425,262 27,874 517,060 26,915,306 1,945,999 24,969,308 2015-2016 11,521,141 10,688,711 30,565 549,131 22,789,548 1,681,234 21,108,314 2016-2017 12,656,947 11,425,055 24,422 589,240 24,695,665 1,692,866 23,002,798 2017-2018 13,559,040 12,222,358 27,101 553,296 26,361,795 1,728,954 24,632,841 2018-2019 13,960,681 12,235,205 24,953 438,805 26,659,643 1,765,610 24,894,033 2019-2020 14,008,718 12,056,082 24,414 439,582 26,528,797 1,747,608 24,781,189 2020-2021 14,240,508 12,121,897 24,414 444,597 26,831,417 1,745,220 25,086,197 2021-2022 14,445,584 12,172,631 24,414 448,907 27,091,536 1,744,135 25,347,401 2022-2023 14,646,746 12,119,553 24,414 451,402 27,242,116 1,743,521 25,498,595 2023-2024 14,844,751 12,080,849 24,414 454,086 27,404,100 1,743,294 25,660,807


DemandC&I

DemandCompany

Use

Firm Unbilled and

Losses

Total Firm After

Unbilled and Losses

Capacity Exempt


Losses

Total Planning

Load

2014-2015 8,730,670 9,334,869 26,915 372,880 18,465,334 2,715,893 15,749,440 2015-2016 7,083,210 9,480,505 20,887 405,542 16,990,145 3,456,703 13,533,442 2016-2017 7,746,054 10,167,207 20,691 440,171 18,374,123 3,724,495 14,649,629 2017-2018 8,287,955 10,735,367 19,744 400,582 19,443,649 4,077,436 15,366,212 2018-2019 8,586,904 10,568,550 32,194 319,603 19,507,251 3,931,722 15,575,529 2019-2020 8,585,253 10,549,556 32,346 322,951 19,490,106 3,850,058 15,640,048 2020-2021 8,624,003 10,558,291 32,346 323,751 19,538,392 3,849,668 15,688,724 2021-2022 8,670,233 10,641,918 32,346 325,939 19,670,435 3,850,094 15,820,341 2022-2023 8,789,976 10,667,297 32,346 328,384 19,818,003 3,850,345 15,967,659 2023-2024 8,868,994 10,718,090 32,346 330,572 19,950,001 3,850,590 16,099,411


DemandC&I

DemandCompany

Use

Firm Unbilled and

Losses

Total Firm After

Unbilled and Losses

Capacity Exempt


Losses

Total Planning

Load

2014-2015 4,851,769 4,420,438 37,824 168,350 9,478,381 1,428,164 8,050,217 2015-2016 4,026,128 3,715,190 34,442 180,631 7,956,391 1,193,366 6,763,025 2016-2017 4,400,785 3,706,813 33,569 193,010 8,334,177 910,525 7,423,652 2017-2018 4,665,823 3,704,417 34,325 180,604 8,585,169 908,280 7,676,890 2018-2019 4,773,742 3,777,555 29,608 143,292 8,724,198 956,192 7,768,007 2019-2020 4,691,222 3,838,083 32,536 144,260 8,706,101 935,844 7,770,257 2020-2021 4,727,981 3,914,756 32,536 146,171 8,821,445 935,961 7,885,484 2021-2022 4,759,862 3,950,623 32,536 147,313 8,890,333 936,109 7,954,224 2022-2023 4,795,471 3,965,903 32,536 148,170 8,942,081 936,209 8,005,872 2023-2024 4,832,913 3,973,493 32,536 148,929 8,987,871 936,270 8,051,601

Brockton

Springfield

Lawrence



Page A-3


DemandC&I

DemandCompany

Use

Firm Unbilled and

Losses

Total Firm After

Unbilled and Losses

Capacity Exempt


Losses

Total Planning

Load

2014-2015 27,527,549 26,180,569 92,614 1,058,289 54,859,021 6,090,056 48,768,965 2015-2016 22,630,479 23,884,406 85,894 1,135,305 47,736,084 6,331,303 41,404,781 2016-2017 24,803,786 25,299,075 78,683 1,222,421 51,403,965 6,327,886 45,076,079 2017-2018 26,512,818 26,662,142 81,171 1,134,482 54,390,613 6,714,670 47,675,943 2018-2019 27,321,327 26,581,310 86,755 901,700 54,891,092 6,653,524 48,237,569 2019-2020 27,285,193 26,443,721 89,296 906,793 54,725,004 6,533,511 48,191,494 2020-2021 27,592,493 26,594,945 89,296 914,519 55,191,254 6,530,849 48,660,405 2021-2022 27,875,678 26,765,171 89,296 922,159 55,652,304 6,530,338 49,121,966 2022-2023 28,232,194 26,752,753 89,296 927,956 56,002,200 6,530,074 49,472,126 2023-2024 28,546,657 26,772,432 89,296 933,586 56,341,972 6,530,154 49,811,818

[1] excludes demand for Leap Year for 2019/20 and 2023/24 split years

Total



Page A-4

TABLE DDBrockton

Effective Degree Day Data

Gas Year [4] Non-Htg Heating Total PeakSeason Season Gas Year Day

2013-2014 1,018 6,025 7,043 68 2014-2015 902 6,164 7,066 73 2015-2016 988 4,763 5,751 75 2016-2017 946 5,262 6,208 61 2017-2018 980 5,287 6,267 72

Normal 978 5,348 6,326 NADesign 978 6,267 7,245 78

Degree Time PeriodCriterion Days Analyzed Method Used

1. Normal Year [1] 6,326 1999-2018 20 years average2. Design Year [2] 7,245 1967-2018 52 years average3. Design Day [3] 78 1967-2018 Cost Benefit Analysis

Notes:[1]: Normal Year - (6326 Effective Degree Days.). The most recent 20 years of Effective Degree Day Datawas averaged to calculate the number of Effective Degree Days that can be expected in the Heating and Non-Heating Seasons.

[2]: Design Year - One in thirty three occurrence of colder than average EDD Nov - MarchOne in thirty three based on Design Day Analysis in [3].

[3]: Design Day - (78 Effective Degree Days) - the coldest day for which the Company plans to meetits firm customers' requirements.

[4]: The four quarters Q4 through Q3 (October through September)



Page A-5

TABLE DDLawrence



2013-2014 1,184 6,393 7,577 71 2014-2015 1,033 6,496 7,529 75 2015-2016 1,114 5,113 6,227 76 2016-2017 1,090 5,603 6,693 65 2017-2018 1,098 5,665 6,763 75

Normal 1,131 5,680 6,811 NADesign 1,131 6,580 7,711 80









Page A-6

TABLE DDSpringfield



2013-2014 807 6,075 6,882 67 2014-2015 700 6,044 6,744 73 2015-2016 884 4,757 5,641 77 2016-2017 827 5,328 6,155 58 2017-2018 926 5,466 6,392 73

Normal 850 5,509 6,359 NADesign 850 6,344 7,194 78









Page A-7

TABLE FAColumbia Gas of Massachusetts

Forecast AccuracyTotal Gas Year Normalized Firm Sendout

ActualNormalized Prior

Gas Year [1] Sendout Forecast(Dth) [2] (Dth) %Difference

2013-2014 52,611,360 48,410,265 [3] 8.68%2014-2015 52,881,245 52,068,507 [4] 1.56%2015-2016 51,518,123 52,978,437 [4] -2.76%2016-2017 51,768,271 52,214,652 [5] -0.85%2017-2018 55,046,555 53,021,797 [5] 3.82%

Notes:[1]: The four quarters Q4 through Q3 (October through September)[2]: Source: Table G-5 CMA[3]: Source: Table G-5 CMA in the CMA F&SP 2013[3]: Prior Forecast Sendout excluded Special Demand.[4]: Source: Table G-5 CMA in the CMA F&SP 2015[4]: Prior Forecast Sendout excluded Special Demand.[5]: Source: Table G-5 CMA in the CMA F&SP 2017[5]: Prior Forecast Sendout excluded Special Demand.



Page A-8

TABLE G-1Columbia Gas of Massachusetts

Residential with Gas Heating

Historical Sendout (Dth)Actual Normal Actual Use / Cust Normal Use / Cust

Gas Year [1] Avg # of Heating Non-Htg Total Heating Non-Htg Total Heating Non-Htg Heating Non-HtgCustomers [2] Season Season Gas Year [1] Season Season Gas Year [1] Season Season Season Season

2013-2014 251,846 19,162,146 7,415,275 26,577,421 17,201,137 6,947,286 24,148,423 76 29 68 28 2014-2015 256,641 19,695,311 7,408,712 27,104,023 17,349,576 7,319,495 24,669,071 77 29 68 29 2015-2016 260,848 15,186,732 7,076,281 22,263,013 17,216,971 7,023,658 24,240,630 58 27 66 27 2016-2017 264,696 16,933,745 7,493,722 24,427,467 17,823,625 7,119,784 24,943,409 64 28 67 27 2017-2018 268,895 18,470,645 7,671,269 26,141,914 18,842,136 7,084,605 25,926,741 69 29 70 26.35

Forecasted Sendout (Dth) [3]Normal Design Normal Use / Cust

Gas Year [1] Avg # of Heating Non-Htg Total Heating Heating Non-HtgCustomers [2] Season Season Gas Year [1] Season Season Season

2018-2019 272,812 19,328,950 7,629,431 26,958,381 22,096,502 70.85 27.972019-2020 276,620 19,178,697 7,755,906 26,934,603 22,277,426 69.33 28.042020-2021 279,466 19,423,938 7,826,854 27,250,792 22,554,791 69.50 28.012021-2022 282,694 19,633,093 7,911,236 27,544,329 22,796,931 69.45 27.992022-2023 286,603 19,904,556 8,007,968 27,912,524 23,111,539 69.45 27.942023-2024 289,974 20,156,058 8,082,778 28,238,837 23,402,571 69.51 27.87

[1]: Calendar twelve months (November to October)[2]: Average number of customers is calculated as annual Dth / annual Dth/customer for four quarters ended Qtr 3;

Qtr4 through Qtr3 (October through September)[3]: Excludes demand for Leap Year for 2019/20 and 2023/24 split years



Page A-9

TABLE G-1Brockton


Sales and Transportation LoadsHistorical Sendout (Dth)

Actual Normal Actual Use / Cust Normal Use / CustGas Year [1] Avg # of Heating Non-Htg Total Heating Non-Htg Total Heating Non-Htg Heating Non-Htg

Customers [2] Season Season Gas Year [1] Season Season Gas Year [1] Season Season Season Season2013-2014 127,510 9,687,778 3,781,602 13,469,380 8,624,302 3,513,553 12,137,855 75.98 29.66 67.64 27.562014-2015 130,192 9,965,714 3,822,990 13,788,704 8,620,980 3,724,973 12,345,953 76.55 29.36 66.22 28.612015-2016 132,665 7,706,723 3,677,370 11,384,093 8,696,637 3,650,884 12,347,521 58.09 27.72 65.55 27.522016-2017 134,962 8,583,459 3,931,729 12,515,188 8,994,520 3,738,401 12,732,921 63.60 29.13 66.64 27.702017-2018 137,782 9,396,135 4,024,494 13,420,629 9,629,303 3,794,674 13,423,977 68.20 29.21 69.89 27.54



2018-2019 139,944 9,857,398 3,969,328 13,826,726 11,359,710 70.44 28.362019-2020 142,003 9,833,431 4,044,858 13,878,289 11,498,431 69.25 28.482020-2021 143,961 10,012,608 4,100,140 14,112,748 11,700,886 69.55 28.482021-2022 145,895 10,171,752 4,150,012 14,321,764 11,882,665 69.72 28.452022-2023 147,842 10,328,635 4,199,494 14,528,129 12,062,214 69.86 28.412023-2024 149,781 10,484,718 4,246,744 14,731,462 12,240,911 70.00 28.35

Notes:[1]: Calendar twelve months (November to October)[2]: Average number of customers is calculated as annual Dth / annual Dth/customer for four quarters ended Qtr 3;




Page A-10

TABLE G-1Lawrence





Normal Design Normal Use / CustGas Year [1] Avg # of Heating Non-Htg Total Heating Heating Non-Htg

Customers [2] Season Season Gas Year [1] Season Season Season2018-2019 43,624 3,377,743 1,347,325 4,725,067 3,818,228 77.43 30.892019-2020 44,037 3,283,386 1,360,820 4,644,206 3,817,682 74.56 30.902020-2021 44,436 3,311,791 1,370,490 4,682,280 3,849,945 74.53 30.842021-2022 44,847 3,332,337 1,383,140 4,715,477 3,873,779 74.30 30.842022-2023 45,232 3,356,736 1,395,783 4,752,519 3,902,697 74.21 30.862023-2024 45,592 3,385,196 1,406,274 4,791,470 3,936,537 74.25 30.84



Forecasted Sendout (Dth) [3]



Page A-11

TABLE G-1Springfield







2018-2019 89,244 6,093,810 2,312,778 8,406,588 6,918,564 68.28 25.922019-2020 90,580 6,061,879 2,350,228 8,412,107 6,961,312 66.92 25.952020-2021 91,068 6,099,539 2,356,225 8,455,764 7,003,960 66.98 25.872021-2022 91,953 6,129,004 2,378,084 8,507,088 7,040,487 66.65 25.862022-2023 93,529 6,219,185 2,412,691 8,631,876 7,146,628 66.50 25.802023-2024 94,601 6,286,144 2,429,760 8,715,904 7,225,123 66.45 25.68





Page A-12


Residential without Gas Heating

Historical Sendout (Dth)Actual Normal Actual Use / Cust Normal Use / Cust

Gas Year [1] Avg # of Heating Non-Htg Total Heating Non-Htg Total Heating Non-Htg Heating Non-HtgCustomers [2] Season Season Gas Year [1] Season Season Gas Year [1] Season Season Season Season

2013-2014 23,022 264,017 215,672 479,689 253,216 213,951 467,166 11.47 9.37 11.00 9.292014-2015 21,653 232,583 190,943 423,526 220,314 191,302 411,616 10.74 8.82 10.17 8.842015-2016 20,751 190,211 177,255 367,466 199,751 177,694 377,444 9.17 8.54 9.63 8.562016-2017 20,301 197,417 178,902 376,319 201,587 178,132 379,719 9.72 8.81 9.93 8.772017-2018 19,780 205,159 165,746 370,905 206,175 162,559 368,734 10.37 8.38 10.42 8.22



2018-2019 19,200 196,545 166,402 362,946 208,736 10.24 8.672019-2020 18,629 187,240 163,351 350,591 200,041 10.05 8.772020-2021 18,072 182,677 159,024 341,701 195,096 10.11 8.802021-2022 17,508 177,257 154,092 331,349 189,285 10.12 8.802022-2023 16,948 171,064 148,605 319,670 182,702 10.09 8.772023-2024 16,387 164,815 143,006 307,821 176,062 10.06 8.73





Page A-13

TABLE G-2Brockton




Customers [2] Season Season Gas Year [1] Season Season Gas Year [1] Season Season Season Season2013-2014 9,238 100,042 80,209 180,251 94,933 78,792 173,725 10.83 8.68 10.28 8.532014-2015 8,658 85,623 70,783 156,406 79,521 70,712 150,233 9.89 8.18 9.19 8.172015-2016 8,337 70,760 66,288 137,048 74,641 66,586 141,227 8.49 7.95 8.95 7.992016-2017 8,136 74,740 67,019 141,759 76,296 66,299 142,595 9.19 8.24 9.38 8.152017-2018 7,883 76,560 61,851 138,411 77,177 61,089 138,267 9.71 7.85 9.79 7.75



2018-2019 7,638 71,782 62,172 133,954 77,357 9.40 8.142019-2020 7,384 69,383 61,046 130,429 75,183 9.40 8.272020-2021 7,131 68,144 59,616 127,760 73,745 9.56 8.362021-2022 6,878 66,189 57,631 123,820 71,592 9.62 8.382022-2023 6,625 63,472 55,145 118,617 68,676 9.58 8.322023-2024 6,372 60,717 52,571 113,289 65,723 9.53 8.25





Page A-14

TABLE G-2Lawrence







2018-2019 2,427 26,318 22,357 48,675 27,928 10.85 9.212019-2020 2,357 24,569 22,446 47,015 26,425 10.42 9.522020-2021 2,288 23,944 21,757 45,701 25,746 10.46 9.512021-2022 2,220 23,270 21,114 44,384 25,018 10.48 9.512022-2023 2,151 22,552 20,400 42,952 24,246 10.48 9.482023-2024 2,083 21,774 19,668 41,443 23,414 10.46 9.44





Page A-15

TABLE G-2Springfield







2018-2019 9,135 98,444 81,872 180,317 103,451 10.78 8.962019-2020 8,887 93,287 79,859 173,146 98,433 10.50 8.992020-2021 8,652 90,589 77,651 168,240 95,605 10.47 8.972021-2022 8,410 87,798 75,347 163,145 92,675 10.44 8.962022-2023 8,172 85,040 73,060 158,101 89,780 10.41 8.942023-2024 7,933 82,323 70,766 153,089 86,925 10.38 8.92





Page A-16

TABLE G-3(C&I LLF)Columbia Gas of Massachusetts

Commercial and Industrial Low-Load Factor



Customers [2] Season Season Gas Year [1] Season Season Gas Year [1] Season Season Season Season2013-2014 24,639 11,781,434 4,132,764 15,914,198 10,611,025 3,870,867 14,481,891 478.16 167.73 430.66 157.10 2014-2015 25,094 12,515,581 4,372,091 16,887,672 11,066,012 4,303,627 15,369,639 498.75 174.23 440.99 171.50 2015-2016 25,442 9,685,087 4,135,066 13,820,153 10,889,469 4,137,795 15,027,264 380.68 162.53 428.02 162.64 2016-2017 25,014 10,145,085 3,892,131 14,037,216 10,726,828 3,735,737 14,462,565 405.57 155.60 428.83 149.34 2017-2018 25,429 11,450,243 4,335,452 15,785,695 11,676,301 4,072,317 15,748,617 450.29 170.49 459.18 160.15


Customers [2] Season Season Gas Year [1] Season Season Season2018-2019 25,751 12,010,648 4,160,666 16,171,313 13,686,625 466.42 161.58 2019-2020 25,929 11,713,110 4,224,631 15,937,741 13,658,504 451.73 162.93 2020-2021 25,955 11,756,744 4,220,603 15,977,347 13,697,359 452.96 162.61 2021-2022 25,959 11,772,095 4,208,111 15,980,206 13,716,731 453.48 162.10 2022-2023 25,925 11,742,356 4,194,767 15,937,123 13,684,837 452.94 161.81 2023-2024 25,894 11,708,922 4,179,262 15,888,184 13,648,750 452.19 161.40






Page A-17

TABLE G-3(C&I LLF)Brockton












Page A-18

TABLE G-3(C&I LLF)Lawrence




Customers [2] Season Season Gas Year [1] Season Season Gas Year [1] Season Season Season Season2013-2014 2,867 1,794,926 702,203 2,497,129 1,571,942 647,786 2,219,727 626.08 244.93 548.30 225.95 2014-2015 2,906 1,902,202 681,717 2,583,919 1,633,087 677,153 2,310,239 654.48 234.56 561.89 232.99 2015-2016 2,981 1,515,555 683,744 2,199,299 1,707,763 702,241 2,410,004 508.33 229.34 572.80 235.54 2016-2017 2,923 1,510,292 636,284 2,146,576 1,588,365 595,869 2,184,234 516.63 217.66 543.34 203.83 2017-2018 2,970 1,693,414 635,733 2,329,147 1,725,537 554,029 2,279,567 570.23 214.07 581.05 186.56


Customers [2] Season Season Gas Year [1] Season Season Season2018-2019 3,017 1,725,473 650,546 2,376,019 1,979,696 571.89 215.612019-2020 3,047 1,709,521 662,009 2,371,531 2,017,630 561.12 217.292020-2021 3,058 1,732,885 662,532 2,395,417 2,042,343 566.67 216.652021-2022 3,059 1,729,634 660,576 2,390,210 2,039,282 565.42 215.942022-2023 3,056 1,721,234 657,454 2,378,689 2,030,610 563.20 215.122023-2024 3,057 1,712,017 655,019 2,367,036 2,021,385 560.04 214.27






Page A-19

TABLE G-3(C&I LLF)Springfield












Page A-20

TABLE G-3(C&I HLF)Columbia Gas of Massachusetts

Commercial and Industrial High-Load Factor



Customers [2] Season Season Gas Year [1] Season Season Gas Year [1] Season Season Season Season2013-2014 5,343 5,104,264 5,336,501 10,440,765 4,929,705 5,273,604 10,203,308 955.38 998.85 922.70 987.07 2014-2015 5,002 4,514,000 4,778,897 9,292,897 4,269,433 4,716,958 8,986,390 902.38 955.33 853.49 942.95 2015-2016 4,946 4,660,726 5,403,527 10,064,253 4,754,157 5,355,425 10,109,582 942.27 1,092.45 961.16 1,082.72 2016-2017 5,694 5,348,320 5,913,542 11,261,862 5,465,697 5,930,612 11,396,310 939.26 1,038.53 959.88 1,041.52 2017-2018 5,508 5,172,780 5,703,667 10,876,447 5,191,588 5,597,266 10,788,854 939.21 1,035.60 942.63 1,016.28








Page A-21

TABLE G-3(C&I HLF)Brockton












Page A-22

TABLE G-3(C&I HLF)Lawrence




Customers [2] Season Season Gas Year [1] Season Season Gas Year [1] Season Season Season Season2013-2014 749 964,843 1,013,636 1,978,479 927,099 998,874 1,925,973 1,288.46 1,353.62 1,238.06 1,333.91 2014-2015 708 910,083 926,436 1,836,519 862,354 920,398 1,782,751 1,285.28 1,308.37 1,217.87 1,299.84 2015-2016 688 724,155 791,736 1,515,891 745,298 790,943 1,536,242 1,052.42 1,150.64 1,083.15 1,149.49 2016-2017 788 795,259 764,978 1,560,237 806,117 753,942 1,560,059 1,009.11 970.68 1,022.88 956.68 2017-2018 774 739,456 635,814 1,375,270 735,213 562,723 1,297,935 955.48 821.56 950.00 727.12


Customers [2] Season Season Gas Year [1] Season Season Season2018-2019 767 689,515 712,022 1,401,537 737,329 898.96 928.302019-2020 771 722,144 744,409 1,466,553 774,257 937.08 965.982020-2021 774 745,569 773,770 1,519,339 797,894 963.80 1000.252021-2022 776 765,797 794,617 1,560,413 818,276 987.01 1024.162022-2023 778 779,076 808,139 1,587,215 831,718 1000.92 1038.262023-2024 782 788,372 818,085 1,606,457 841,229 1008.56 1046.57






Page A-23

TABLE G-3(C&I HLF)Springfield




Customers [2] Season Season Gas Year [1] Season Season Gas Year [1] Season Season Season Season2013-2014 1,811 1,879,083 2,012,205 3,891,288 1,832,576 1,998,561 3,831,137 1,037.59 1,111.10 1,011.91 1,103.57 2014-2015 1,667 1,637,883 1,829,791 3,467,674 1,590,671 1,834,588 3,425,259 982.53 1,097.66 954.21 1,100.53 2015-2016 1,649 2,139,656 2,578,798 4,718,454 2,194,593 2,576,594 4,771,186 1,297.48 1,563.78 1,330.80 1,562.44 2016-2017 1,870 2,320,334 2,753,243 5,073,577 2,349,996 2,739,247 5,089,243 1,240.77 1,472.26 1,256.63 1,464.77 2017-2018 1,809 2,284,441 2,850,145 5,134,586 2,288,669 2,822,036 5,110,704 1,262.82 1,575.54 1,265.16 1,560.00








Page A-24

TABLE G-3(CE)Brockton


Transportation LoadsHistorical Sendout (Dth)

Actual NormalGas Year [1] Heating Non-Htg Total Heating Non-Htg Total

Season Season Gas Year [1] Season Season Gas Year [1]2013-2014 1,573,949 982,470 2,556,419 1,495,446 963,578 2,459,023 2014-2015 1,178,493 764,093 1,942,586 1,079,479 765,625 1,845,104 2015-2016 973,646 700,576 1,674,222 1,040,469 706,233 1,746,702 2016-2017 999,912 711,761 1,711,673 1,027,484 701,854 1,729,338 2017-2018 1,004,500 688,438 1,692,938 1,017,099 676,180 1,693,278

Normal DesignGas Year [1] Heating Non-Htg Total Heating

Season Season Gas Year [1] Season2018-2019 1,048,805 695,214 1,744,019 1,151,517 2019-2020 1,025,048 706,976 1,732,024 1,136,169 2020-2021 1,017,963 712,047 1,730,010 1,129,084 2021-2022 1,014,908 714,234 1,729,142 1,126,029 2022-2023 1,013,591 715,177 1,728,768 1,124,712 2023-2024 1,013,023 715,583 1,728,606 1,124,144






Page A-25

TABLE G-3(CE)Lawrence


Transportation LoadsHistorical Sendout (Dth)


Season Season Gas Year [1] Season Season Gas Year [1]2013-2014 997,037 664,322 1,661,360 957,404 654,696 1,612,100 2014-2015 871,544 551,522 1,423,067 821,444 552,349 1,373,792 2015-2016 695,008 497,057 1,192,065 728,808 500,149 1,228,958 2016-2017 573,622 340,030 913,652 587,362 334,530 921,892 2017-2018 536,529 337,525 874,055 543,393 277,834 821,227


Season Season Gas Year [1] Season2018-2019 555,841 390,078 945,919 601,921 2019-2020 537,684 390,078 927,763 592,594 2020-2021 537,684 390,078 927,763 592,594 2021-2022 537,684 390,078 927,763 592,594 2022-2023 537,684 390,078 927,763 592,594 2023-2024 537,684 390,078 927,763 592,594






Page A-26

TABLE G-3(CE)Springfield




Season Season Gas Year [1] Season Season Gas Year [1]2013-2014 1,863,376 1,293,149 3,156,526 1,773,116 1,282,771 3,055,887 2014-2015 1,572,169 1,142,900 2,715,069 1,479,899 1,165,607 2,645,505 2015-2016 1,768,977 1,653,609 3,422,586 1,893,940 1,659,449 3,553,388 2016-2017 2,035,078 1,718,079 3,753,156 2,092,683 1,700,587 3,793,269 2017-2018 2,096,144 1,903,094 3,999,237 2,112,069 1,867,406 3,979,474


Season Season Gas Year [1] Season2018-2019 2,111,736 1,780,697 3,892,434 2,245,907 2019-2020 2,032,231 1,780,697 3,812,928 2,176,586 2020-2021 2,032,231 1,780,697 3,812,928 2,176,586 2021-2022 2,032,231 1,780,697 3,812,928 2,176,586 2022-2023 2,032,231 1,780,697 3,812,928 2,176,586 2023-2024 2,032,231 1,780,697 3,812,928 2,176,586






Page A-27

TABLE G-4(A)Columbia Gas of Massachusetts

Notes:[1]: Interruptible Load ended in April 2010.

Interruptible [1]



Page A-28

TABLE G-4(B)Columbia Gas of Massachusetts

Notes:[1]: No Sales for Resale in this filing

Sales for Resale (Firm) [1]



Page A-29

TABLE G-4(C)Columbia Gas of Massachusetts

Company Use, Unbilled, and Unaccounted For

Actual NormalGas Year [1] Heating Non-Htg Heating Non-Htg

Season Season Season Season2013-2014 3,381,444 (2,386,572) 3,381,444 (2,386,572) 2014-2015 3,471,860 (2,215,945) 3,471,860 (2,215,945) 2015-2016 2,105,613 (1,240,505) 2,105,613 (1,240,505) 2016-2017 3,380,975 (2,794,707) 3,380,975 (2,794,707) 2017-2018 3,737,851 (1,524,242) 3,737,851 (1,524,242)

Normal DesignGas Year [1] Heating Non-Htg Heating

Season Season Season2018-2019 1,691,456 (575,571) 2,745,857 2019-2020 1,566,567 (570,477) 2,597,430 2020-2021 1,572,364 (568,548) 2,603,673 2021-2022 1,577,406 (565,951) 2,609,328 2022-2023 1,581,727 (564,474) 2,614,330 2023-2024 1,585,673 (562,790) 2,618,890

Notes:[1]: Calendar twelve months (November to October)[2]: Excludes demand for Leap Year for 2019/20 and 2023/24 split years


Season Season Season Season2013-2014 404,126 (408,985) 404,126 (408,985) 2014-2015 330,219 (320,884) 330,219 (320,884) 2015-2016 228,690 (186,260) 228,690 (186,260) 2016-2017 351,489 (402,084) 351,489 (402,084) 2017-2018 343,413 (194,973) 343,413 (194,973)


Season Season Season2018-2019 158,271 (87,119) 246,414 2019-2020 145,575 (84,779) 228,805 2020-2021 144,538 (84,390) 227,060 2021-2022 143,797 (83,292) 225,691 2022-2023 143,170 (82,555) 224,481 2023-2024 142,638 (81,781) 223,453





Unbilled and Unaccounted ForCapacity Exempt

Historical Sendout (Dth)



Page A-30

TABLE G-4(C)Brockton


Season Season Season Season2013-2014 1,671,297 (1,213,670) 1,671,297 (1,213,670) 2014-2015 1,777,799 (1,166,673) 1,777,799 (1,166,673) 2015-2016 1,099,094 (731,455) 1,099,094 (731,455) 2016-2017 1,726,389 (1,470,679) 1,726,389 (1,470,679) 2017-2018 2,165,077 (1,089,300) 2,165,077 (1,089,300)


Season Season Season2018-2019 885,355 (333,078) 1,511,156 2019-2020 796,298 (332,301) 1,407,953 2020-2021 799,751 (330,740) 1,411,860 2021-2022 803,041 (329,720) 1,415,539 2022-2023 805,344 (329,528) 1,418,201 2023-2024 807,481 (328,980) 1,420,699


Company Use and Unaccounted ForCapacity Exempt

Historical Sendout (Dth)Actual Normal

Gas Year [1] Heating Non-Htg Heating Non-HtgSeason Season Season Season

2013-2014 146,494 (146,491) 146,494 (146,491) 2014-2015 113,979 (110,566) 113,979 (110,566) 2015-2016 73,202 (66,190) 73,202 (66,190) 2016-2017 108,444 (127,251) 108,444 (127,251) 2017-2018 125,048 (89,032) 125,048 (89,032)


Season Season Season2018-2019 50,812 (29,222) 84,048 2019-2020 45,068 (29,484) 76,912 2020-2021 44,443 (29,233) 75,794 2021-2022 44,018 (29,025) 75,002 2022-2023 43,761 (29,007) 74,498 2023-2024 43,553 (28,866) 74,075


Company Use and Unaccounted For






Page A-31

TABLE G-4(C)Lawrence



Season Season Season Season2013-2014 674,720 (467,845) 674,720 (467,845) 2014-2015 633,389 (409,727) 633,389 (409,727) 2015-2016 353,225 (237,671) 353,225 (237,671) 2016-2017 649,692 (570,391) 649,692 (570,391) 2017-2018 588,910 (132,519) 588,910 (132,519)

Forecasted Sendout (Dth) [2]Normal Design

Gas Year [1] Heating Non-Htg HeatingSeason Season Season

2018-2019 298,635 (102,903) 427,143 2019-2020 278,731 (101,935) 404,631 2020-2021 279,987 (101,280) 405,978 2021-2022 280,608 (100,759) 406,659 2022-2023 281,089 (100,383) 407,214 2023-2024 281,557 (100,092) 407,775





2013-2014 105,726 (104,236) 105,726 (104,236) 2014-2015 84,405 (79,308) 84,405 (79,308) 2015-2016 46,900 (45,598) 46,900 (45,598) 2016-2017 68,112 (71,238) 68,112 (71,238) 2017-2018 53,252 (19,027) 53,252 (19,027)



2018-2019 27,200 (16,928) 37,884 2019-2020 24,529 (16,447) 34,606 2020-2021 24,331 (16,132) 34,314 2021-2022 24,235 (15,889) 34,169 2022-2023 24,161 (15,715) 34,052 2023-2024 24,091 (15,583) 33,935





Page A-32

TABLE G-4(C)Springfield




Season Season Season Season2013-2014 1,035,428 (705,056) 1,035,428 (705,056) 2014-2015 1,060,672 (639,545) 1,060,672 (639,545) 2015-2016 653,294 (271,379) 653,294 (271,379) 2016-2017 1,004,894 (753,637) 1,004,894 (753,637) 2017-2018 983,864 (302,424) 983,864 (302,424)



2018-2019 507,466 (139,590) 807,557 2019-2020 491,538 (136,241) 784,846 2020-2021 492,625 (136,528) 785,835 2021-2022 493,757 (135,472) 787,130 2022-2023 495,293 (134,563) 788,915 2023-2024 496,635 (133,718) 790,416





2013-2014 151,906 (158,258) 151,906 (158,258) 2014-2015 131,834 (131,010) 131,834 (131,010) 2015-2016 108,588 (74,472) 108,588 (74,472) 2016-2017 174,934 (203,595) 174,934 (203,595) 2017-2018 165,113 (86,914) 165,113 (86,914)



2018-2019 80,258 (40,970) 124,481 2019-2020 75,979 (38,848) 117,286 2020-2021 75,765 (39,025) 116,952 2021-2022 75,544 (38,379) 116,520 2022-2023 75,249 (37,832) 115,931 2023-2024 74,994 (37,332) 115,443




Page A-33

TABLE G-5 (A)Columbia Gas of Massachusetts

Total Firm Company Sendout(Including Company Use and Unaccounted For)


ActualGas Year [1] Heating Non-Htg Total Heating Non-Htg Total



Gas Year [1] Heating Non-Htg Total HeatingSeason Season Gas Year [1] Season

2018-2019 38,220,483 16,798,040 55,018,522 43,948,681 2019-2020 37,619,564 17,105,440 54,725,004 43,942,670 2020-2021 37,969,402 17,221,851 55,191,254 44,319,468 2021-2022 38,273,715 17,378,590 55,652,304 44,660,704 2022-2023 38,534,462 17,467,738 56,002,200 44,962,589 2023-2024 38,772,631 17,569,341 56,341,972 45,237,756


Normal



Page A-34

TABLE G-5 (A)Brockton







2018-2019 18,881,423 7,866,739 26,748,162 22,025,905 2019-2020 18,615,145 7,913,652 26,528,797 22,012,240 2020-2021 18,823,567 8,007,850 26,831,417 22,248,056 2021-2022 19,022,114 8,069,422 27,091,536 22,470,080 2022-2023 19,161,092 8,081,024 27,242,116 22,630,749 2023-2024 19,290,048 8,114,053 27,404,100 22,781,483


Normal



Page A-35

TABLE G-5 (A)Lawrence







2018-2019 6,117,683 2,629,347 8,747,030 6,990,325 2019-2020 6,018,352 2,687,750 8,706,101 7,040,625 2020-2021 6,094,176 2,727,269 8,821,445 7,121,906 2021-2022 6,131,646 2,758,687 8,890,333 7,163,014 2022-2023 6,160,688 2,781,393 8,942,081 7,196,484 2023-2024 6,188,917 2,798,954 8,987,871 7,230,341


Normal



Page A-36

TABLE G-5 (A)Springfield







2018-2019 13,221,377 6,301,954 19,523,330 14,932,452 2019-2020 12,986,067 6,504,039 19,490,106 14,889,804 2020-2021 13,051,659 6,486,733 19,538,392 14,949,507 2021-2022 13,119,954 6,550,481 19,670,435 15,027,609 2022-2023 13,212,683 6,605,321 19,818,003 15,135,355 2023-2024 13,293,666 6,656,335 19,950,001 15,225,932


Normal



Page A-37

TABLE G-5 (B)Columbia Gas of Massachusetts

Total Firm Company Planning Load(Including Company Use and Unaccounted For)

Sales and Transportation LoadsHistorical Planning Load (Dth)

Actual Normal ActualGas Year [1] Heating Non-Htg Total Heating Non-Htg Total Peak

Season Season Gas Year [1] Season Season Gas Year [1] Day [2]2013-2014 34,854,816 12,182,683 47,037,500 25,978,419 13,813,647 39,792,066 433,257 2014-2015 36,476,910 12,397,067 48,873,977 26,978,350 14,368,684 41,347,035 425,598 2015-2016 28,162,049 12,886,641 41,048,690 27,927,935 14,015,001 41,942,936 458,778 2016-2017 32,045,441 12,315,804 44,361,245 27,364,013 14,629,378 41,993,391 393,621 2017-2018 35,056,092 13,617,808 48,673,899 30,375,984 14,290,301 44,666,285 476,622

Forecasted Planning Load (Dth) [4]Normal Design

Gas Year [1] Heating Non-Htg Total Heating DesignSeason Season Gas Year [1] Season Day [3]

2018-2019 34,345,830 14,019,169 48,364,999 39,702,922 509,786 2019-2020 33,879,026 14,312,468 48,191,494 39,808,517 517,482 2020-2021 34,236,986 14,423,419 48,660,405 40,194,144 522,255 2021-2022 34,545,094 14,576,872 49,121,966 40,539,804 527,083 2022-2023 34,807,786 14,664,340 49,472,126 40,844,216 530,864 2023-2024 35,047,055 14,764,763 49,811,818 41,120,979 534,503

Notes:[1]: Calendar twelve months (November to October)[2]: Actual Peak Day Demand May include interruptible and special demand.[3]: Design Day for Planning Load excludes Capacity Exempt Trans and DSM.[4]: Excludes demand for Leap Year for 2019/20 and 2023/24 split years



Page A-38

TABLE G-5 (B)Brockton







2018-2019 17,781,805 7,200,747 24,982,552 20,790,339 274,137 2019-2020 17,545,029 7,236,160 24,781,189 20,799,159 274,763 2020-2021 17,761,161 7,325,035 25,086,197 21,043,178 278,072 2021-2022 17,963,188 7,384,213 25,347,401 21,269,049 280,908 2022-2023 18,103,740 7,394,854 25,498,595 21,431,539 282,643 2023-2024 18,233,471 7,427,335 25,660,807 21,583,264 284,487




Page A-39

TABLE G-5 (B)Lawrence







2018-2019 5,534,642 2,256,196 7,790,838 6,350,519 80,540 2019-2020 5,456,139 2,314,118 7,770,257 6,413,425 82,631 2020-2021 5,532,161 2,353,323 7,885,484 6,494,998 83,774 2021-2022 5,569,727 2,384,497 7,954,224 6,536,252 84,464 2022-2023 5,598,842 2,407,030 8,005,872 6,569,839 85,001 2023-2024 5,627,142 2,424,459 8,051,601 6,603,812 85,493




Page A-40

TABLE G-5 (B)Springfield







2018-2019 11,029,382 4,562,226 15,591,608 12,562,063 155,109 2019-2020 10,877,858 4,762,190 15,640,048 12,595,932 160,088 2020-2021 10,943,663 4,745,061 15,688,724 12,655,968 160,408 2021-2022 11,012,180 4,808,162 15,820,341 12,734,503 161,711 2022-2023 11,105,203 4,862,456 15,967,659 12,842,838 163,220 2023-2024 11,186,442 4,912,969 16,099,411 12,933,903 164,524




Page A-41


Notes:[1]: Please see the text of section III of the report for a description of the impact of causative variables on use factors.

Impact of Causative Variables on Use Factors [1]



Page A-42

TABLE G-14

Columbia Gas of MassachusettsExisiting On-System Peaking Resources

CapacityDivision No. Tanks Gallons Total Vaporization

LNG Facility Liquid MMBtu MDWQper Tank (net of heel)

Easton Brockton 1 9,393,300 731,704 44,000Lawrence Lawrence 5 30,208 11,628 12,500Ludlow Springfield 1 12,173,947 948,413 48,000Marshfield Brockton 2 49,500 7,622 8,000

Total Brockton 739,326 52,000Total SP/LAW 960,041 60,500

Total 1,699,367 112,500

Propane Facility

Meadowlane Brockton 12 72,297 70,749 21,000Lawrence Lawrence 3 53,505 13,033 14,000N. Hampton Springfield 5 53,505 21,722 5,000West Springf Springfield 4 72,297 23,583 18,000

Total Brockton 70,749 21,000Total SP/LAW 58,338 37,000

Total 129,087 58,000



Page A-43

Table G - 15: Participation in or service from manufacturing and storage facilities planned outside of Massachusetts.Not Applicable

Table G - 16: Exempt and approved manufacturing and storage facilities in Massachusetts and not yet in operation.Not Applicable

Table G - 17: Proposed manufacturing and storage facilities in Massachusetts.Not Applicable

Table G - 21: Proposed pipeline in Massachusetts over a mile in length and over 100 PSI.Not Applicable



Page A-44

Table G-22 NPage 1 of 4

Winter 19-20 Winter 20-21 Winter 21-22 Winter 22-23 Winter 23-24

Planning Load 34,949,035 35,038,357 35,370,834 35,622,685 36,124,644

ResourcesPipeline Tennessee Pipeline -Longhaul 1,937,696 1,924,948 1,924,948 1,924,948 1,937,696 -Niagara 1,558,304 1,548,052 1,548,052 1,548,052 1,558,304 -ITS 0 0 0 0 0 -Portland / Granite 6,627,270 8,048,773 8,012,038 8,020,344 8,117,017 -Dracut 3,174,149 1,900,166 1,960,481 1,975,289 1,958,573

Algonquin Pipeline -Centerville 3,611,549 3,697,234 3,933,782 4,132,273 4,279,960 -AIM 3,759,679 3,870,103 3,875,344 3,889,412 4,009,430 -Hubline 7,270 0 0 749 19,862 -Lambertville 5,393,139 5,568,940 5,604,070 5,594,481 5,676,141

Total Pipeline 26,069,056 26,558,216 26,858,715 27,085,549 27,556,983

Storage -TGP 1,011,512 1,006,360 1,012,811 1,017,921 1,023,008

-National Fuel 1,020,597 1,020,597 1,020,597 1,020,597 1,020,597

-Enbridge Via PNGTS 1,493,868 1,492,429 1,492,396 1,492,433 1,492,420

-Enbridge Via Iroquois 1,676,197 1,677,882 1,678,383 1,678,929 1,679,440

-Dominion 1,323,635 1,324,156 1,324,572 1,324,821 1,325,097

-Tetco 1,548,732 1,548,732 1,548,732 1,548,732 1,548,732

Total Storage 8,074,540 8,070,156 8,077,491 8,083,432 8,089,294

Peaking

-LNG/Propane (On System) 805,439 409,986 434,629 453,704 478,367

- Incremental Resources 0 0 0 0 0

Total Peaking 805,439 409,986 434,629 453,704 478,367

Total Resources 34,949,035 35,038,357 35,370,834 35,622,685 36,124,644

Columbia Gas of Massachuetts2019 F&SP FILING

Requirements vs. ResourcesBase Case - Norm Year

(MMBtu)



Page A-45


Summer 2020 Summer 2021 Summer 2022 Summer 2023 Summer 2024

Planning Load 13,491,695 13,622,049 13,751,141 13,849,412 13,944,700

ResourcesPipeline Tennessee Pipeline -Longhaul 32,230 17,787 18,758 19,728 21,223 -Niagara 2,193,928 2,193,928 2,193,928 2,193,928 2,193,928 -ITS 0 0 0 0 0 -Portland / Granite 2,440,401 2,483,086 2,546,092 2,585,875 2,584,968 -Dracut 1,704,673 1,526,685 1,517,763 1,528,370 1,574,062

Algonquin Pipeline -Centerville 3,018,242 3,078,202 3,129,023 3,158,283 3,189,724 -AIM 3,471,975 3,480,586 3,488,430 3,493,116 3,498,141 -Hubline 0 0 0 0 0 -Lambertville 174,835 374,850 389,167 399,759 408,626

Total Pipeline 13,036,285 13,155,124 13,283,162 13,379,059 13,470,672

Storage -TGP 0 0 0 0 0

-National Fuel 0 0 0 0 0

-Enbridge Via PNGTS 54,098 38,469 40,992 43,359 46,341

-Enbridge Via Iroquois 44,573 71,719 70,249 70,256 70,949

-Dominion 0 0 0 0 0

-Tetco 0 0 0 0 0

Total Storage 98,672 110,187 111,241 113,615 117,290

Peaking


-Incremental Resources 0 0 0 0 0

Total Peaking 356,738 356,738 356,738 356,738 356,738

Total Resources 13,491,695 13,622,049 13,751,141 13,849,412 13,944,700


Requirements vs. ResourcesBase Case - Norm Year

(MMBtu)



Page A-46



Planning Load 36,046,251 36,400,537 36,960,401 37,391,872 38,113,012

ResourcesPipeline Tennessee Pipeline -Longhaul 1,937,696 1,924,948 1,924,948 1,924,948 1,937,696 -Niagara 1,558,304 1,548,052 1,548,052 1,548,052 1,558,304 -ITS 44,595 0 0 0 0 -Portland / Granite 6,916,100 8,472,267 8,578,604 8,810,679 9,154,569 -Dracut 3,171,903 1,933,456 1,984,276 1,953,554 1,955,135

Algonquin Pipeline -Centerville 3,881,083 4,285,126 4,523,488 4,637,369 4,805,956 -AIM 3,789,297 3,985,224 3,986,851 3,993,436 4,029,620 -Hubline 25,095 8,796 48,406 87,065 107,951 -Lambertville 5,659,731 5,622,361 5,685,352 5,702,439 5,764,196

Total Pipeline 26,983,802 27,780,231 28,279,977 28,657,542 29,313,427

Storage -TGP 1,038,550 1,040,663 1,051,330 1,058,656 1,068,243

-National Fuel 1,020,597 1,020,597 1,020,597 1,020,597 1,020,597



-Dominion 1,324,587 1,325,327 1,325,912 1,326,338 1,326,785

-Tetco 1,548,732 1,548,732 1,548,732 1,548,732 1,548,732

Total Storage 8,103,907 8,106,522 8,118,394 8,126,180 8,136,213

Peaking



Total Peaking 958,542 513,784 562,030 608,150 663,371

Total Resources 36,046,251 36,400,537 36,960,401 37,391,872 38,113,011


Requirements vs. ResourcesHigh Case - Norm Year

(MMBtu)



Page A-47



Planning Load 13,916,161 14,153,742 14,372,168 14,540,656 14,716,388



Total Pipeline 13,470,988 13,676,530 13,907,905 14,080,401 14,267,550





-Dominion 0 0 0 0 0

-Tetco 2,514 0 0 0 0

Total Storage 88,435 120,474 107,525 103,517 92,100

Peaking



Total Peaking 356,738 356,738 356,738 356,738 356,738

Total Resources 13,916,161 14,153,742 14,372,168 14,540,656 14,716,388


Requirements vs. ResourcesHigh Case - Norm Year

(MMBtu)



Page A-48

Table G-22 DPage 1 of 4


Total Requirements 42,147,491 42,414,536 43,048,654 43,546,053 44,494,147



Total Pipeline 31,904,644 32,163,647 32,778,796 33,264,458 34,201,899

Storage -TGP 1,143,925 1,146,630 1,157,845 1,157,845 1,157,845

-National Fuel 1,020,597 1,020,597 1,020,597 1,020,597 1,020,597



-Dominion 1,336,225 1,336,263 1,336,199 1,336,158 1,336,291

-Tetco 1,534,852 1,537,873 1,540,508 1,542,290 1,543,205

Total Storage 8,209,356 8,212,643 8,226,461 8,228,224 8,229,038

Peaking

-LNG/Propane (On System) 2,031,321 2,031,321 2,031,321 2,031,321 2,031,321

-Incremental Resources 2,170 6,925 12,075 22,050 31,889

Total Peaking 2,033,491 2,038,246 2,043,396 2,053,371 2,063,210

Total Resources 42,147,491 42,414,536 43,048,654 43,546,053 44,494,147


Requirements vs. ResourcesHigh Case - Design Year

(MMBtu)



Page A-49



Planning Load 13,964,126 14,190,291 14,403,350 14,570,827 14,744,431



Total Pipeline 13,506,820 13,711,830 13,930,592 14,091,641 14,280,342





-Dominion 0 0 0 0 0

-Tetco 8,283 0 0 0 0

Total Storage 100,568 121,722 116,020 122,448 107,351

Peaking



Total Peaking 356,738 356,738 356,738 356,738 356,738

Total Resources 13,964,126 14,190,291 14,403,350 14,570,827 14,744,431


Requirements vs. ResourcesHigh Case - Design Year

(MMBtu)



Page A-50



Planning Load 40,969,097 40,940,807 41,318,319 41,615,962 42,313,636



Total Pipeline 30,841,925 31,344,316 31,533,723 31,748,260 32,173,145

Storage -TGP 1,113,654 1,110,040 1,115,985 1,122,786 1,128,658

-National Fuel 1,020,597 1,020,597 1,020,597 1,020,597 1,020,597



-Dominion 1,336,230 1,336,246 1,336,246 1,336,248 1,336,229

-Tetco 1,542,290 1,542,290 1,540,890 1,541,993 1,537,252

Total Storage 8,186,433 8,180,342 8,184,967 8,192,832 8,193,804

Peaking


-Incremental Resources 0 0 2,369 4,104 5,948

Total Peaking 1,940,739 1,416,149 1,599,629 1,674,871 1,946,687

Total Resources 40,969,097 40,940,807 41,318,319 41,615,962 42,313,636


Requirements vs. ResourcesBase Case - Design Year

(MMBtu)



Page A-51



Planning Load 13,571,561 13,693,540 13,819,527 13,919,654 14,015,531



Total Pipeline 13,115,009 13,217,714 13,343,008 13,441,926 13,536,653





-Dominion 0 0 0 0 0

-Tetco 0 0 0 0 0

Total Storage 99,814 119,088 119,781 120,990 122,140

Peaking



Total Peaking 356,738 356,738 356,738 356,738 356,738

Total Resources 13,571,561 13,693,540 13,819,527 13,919,654 14,015,531


Requirements vs. ResourcesBase Case - Design Year

(MMBtu)



Page A-52

Table G-22 CSPage 1 of 4


Planning Load 37,704,959 37,822,178 38,181,342 38,452,805 38,973,968



Total Pipeline 27,716,173 28,502,720 28,776,290 28,998,258 29,408,673

Storage -TGP 1,011,512 1,006,360 1,012,811 1,017,921 1,023,008

-National Fuel 1,020,597 1,020,597 1,020,597 1,020,597 1,020,597



-Dominion 1,323,641 1,324,156 1,324,572 1,324,821 1,325,097

-Tetco 1,548,732 1,548,732 1,548,732 1,548,732 1,548,732

Total Storage 8,076,317 8,071,101 8,078,076 8,083,397 8,088,754

Peaking


-Incremental Resources 0 2,671 5,599 7,895 12,356

Total Peaking 1,912,469 1,248,357 1,326,976 1,371,149 1,476,539

Total Resources 37,704,959 37,822,178 38,181,342 38,452,805 38,973,967


Requirements vs. ResourcesBase Case - Cold Snap

(MMBtu)



Page A-53



Planning Load 13,491,695 13,622,049 13,751,141 13,849,412 13,944,700



Total Pipeline 13,036,285 13,155,124 13,283,162 13,379,059 13,470,672





-Dominion 0 0 0 0 0

-Tetco 0 0 0 0 0

Total Storage 98,672 110,187 111,241 113,615 117,290

Peaking



Total Peaking 356,738 356,738 356,738 356,738 356,738

Total Resources 13,491,695 13,622,049 13,751,141 13,849,412 13,944,700


Requirements vs. ResourcesBase Case - Cold Snap

(MMBtu)



Page A-54



Planning Load 38,886,406 39,289,284 39,893,036 40,357,992 41,113,892



Total Pipeline 28,751,367 29,438,212 29,799,485 30,148,877 30,880,897

Storage -TGP 1,038,550 1,040,663 1,051,330 1,058,656 1,070,721

-National Fuel 1,020,597 1,020,597 1,020,597 1,020,597 1,020,597



-Dominion 1,324,594 1,325,327 1,325,912 1,326,338 1,326,785

-Tetco 1,548,732 1,548,732 1,548,732 1,548,732 1,548,732

Total Storage 8,106,160 8,106,664 8,117,779 8,125,500 8,137,925

Peaking



Total Peaking 2,028,879 1,744,408 1,975,771 2,083,615 2,095,070

Total Resources 38,886,406 39,289,284 39,893,036 40,357,992 41,113,892


Requirements vs. ResourcesHigh Case - Cold Snap

(MMBtu)



Page A-55



Planning Load 13,916,161 14,153,742 14,372,168 14,540,656 14,716,388



Total Pipeline 13,470,988 13,676,529 13,907,903 14,080,402 14,267,550





-Dominion 0 0 0 0 0

-Tetco 2,514 0 0 0 0

Total Storage 88,435 120,475 107,527 103,517 92,100

Peaking



Total Peaking 356,738 356,738 356,738 356,738 356,738

Total Resources 13,916,161 14,153,742 14,372,168 14,540,656 14,716,388


Requirements vs. ResourcesHigh Case - Cold Snap

(MMBtu)



Page A-56

Table G-23 DDPage 1 of 2


Planning Load 517,482 522,254 527,083 530,864 534,504

ResourcesPipeline Tennessee Pipeline -Longhaul 12,748 12,748 12,748 12,748 12,748 -Niagara 10,252 10,252 10,252 10,252 10,252 -ITS 4,903 0 0 0 0 -Portland / Granite 52,325 92,738 92,738 92,738 92,738 -Dracut 23,100 14,100 14,100 14,100 14,100

Algonquin Pipeline -Centerville 48,000 48,000 48,000 48,000 48,000 -AIM 30,000 30,000 30,000 30,000 30,000 -Hubline 20,000 20,000 20,000 20,000 20,000 -Lambertville 47,028 47,053 47,053 47,053 47,053

Total Pipeline 248,356 274,892 274,892 274,892 274,892

Storage -TGP 18,057 18,057 18,057 18,057 18,057

-National Fuel 9,865 9,865 9,865 9,865 9,865



-Dominion 14,492 14,492 14,492 14,492 14,492

-Tetco 19,986 19,986 19,986 19,986 19,986

Total Storage 102,402 102,363 102,363 102,363 102,363

Peaking


-Incremental Resources 0 0 2,369 4,104 5,948

Total Peaking 166,724 144,999 149,828 153,609 157,249

Total Resources 517,482 522,254 527,083 530,864 534,504

Columbia Gas of Massachusetts2019 F&SP FILING

Requirements vs. ResourcesDesign Day - Base Case

(MMBtu)



Page A-57

Table G-23 DDPage 2 of 2


Planning Load 532,277 540,927 548,995 555,311 561,846

ResourcesPipeline Tennessee Pipeline -Longhaul 12,748 12,748 12,748 12,748 12,748 -Niagara 10,252 10,252 10,252 10,252 10,252 -ITS 13,752 0 0 0 0 -Portland / Granite 52,325 92,738 92,738 92,738 92,738 -Dracut 23,100 14,100 14,100 14,100 14,100

Algonquin Pipeline -Centerville 48,000 48,000 48,000 48,000 48,000 -AIM 30,000 30,000 30,000 30,000 30,000 -Hubline 20,000 20,000 20,000 20,000 20,000 -Lambertville 47,028 47,053 47,053 47,213 47,213

Total Pipeline 257,205 274,892 274,892 275,051 275,051

Storage -TGP 18,057 18,057 18,057 18,057 18,057

-National Fuel 9,865 9,865 9,865 9,865 9,865



-Dominion 14,492 14,492 14,492 14,492 14,492

-Tetco 19,986 19,986 19,986 19,826 19,826

Total Storage 102,402 102,363 102,363 102,204 102,204

Peaking



Total Peaking 172,670 163,672 171,740 178,056 184,591

Total Resources 532,277 540,927 548,995 555,311 561,846

Columbia Gas of Massachusetts2019 F&SP FILING

Requirements vs. ResourcesDesign Day - High Case

(MMBtu)



Page A-58

Columbia Gas of MassachusettsLong Term Contracts as of November 1, 2019

Pipeline ContractRate

Schedule MDQ ACQ DaysContract

ExpirationRenewal Notice

Evergreen Provision

(Y/N)

ROFR Provision

(Y/N)

Contracts for which CMA is requesting

approval in this Current Docket /1 Original Docket

Original Start Date Original Term Extension Notice Dockets and Term

Last Approved Docket

Algonquin 799993 AFT-1H 5,000 910,000 182 4/30/2020 None N N Y 11/1/2019 < 1 yrAlgonquin 799992 AFT-1H 5,000 910,000 182 4/30/2020 None N N Y 11/1/2019 < 1 yrAlgonquin 799991 AFT-1H 5,000 920,000 184 10/31/2020 10/31/2019 N N Y 5/1/2020 < 1 yrAlgonquin 799990 AFT-1H 5,000 920,000 184 10/31/2020 10/31/2019 N N Y 5/1/2020 < 1 yrAlgonquin 93001EC AFT-E 51,632 15,467,350 365 10/31/2020 10/31/2019 N Y Y 9/4/1994 18yrs 2 mos DPU 10-134;DPU 11-89; DPU 12-64; DPU 13-161 DPU 17-166Algonquin 93201AC AFT-1 5,489 2,003,485 365 10/31/2020 10/31/2019 N Y Y 11/1/1993 19 years DPU 10-134; DPU 11-89; DPU 12-64; DPU 13-161 DPU 17-166Algonquin 93401 AFT-1 5,690 2,076,850 365 10/31/2020 10/31/2019 N Y Y 6/1/1993 19yrs 4mos DPU 10-134; DPU 11-89; DPU 12-64; DPU 13-161 DPU 17-166Algonquin 93001F AFT-1 18,490 6,748,850 365 10/31/2022 10/31/2021 N Y Y 11/1/1993 19 years DPU 13-161 DPU 13-161Algonquin 94501 AFT-1 14,758 5,386,670 365 10/31/2020 10/31/2019 N Y Y 11/1/1994 20 years DPU 12-64; DPU 13-161 DPU 17-166

Algonquin 510066 AFT-1 20,000 7,300,000 365 11/30/2023 11/30/2022 N N N DTE 03-37 11/1/2003 10 years DPU 12-04 (5-year renewal term approved) DPU 17-85Algonquin 510352 AFT-1(X-35) 48,000 17,520,000 365 10/31/2023 10/31/2022 N Y N DTE 06-7 1/1/2009 Init 5 yr -5yr opt DTE 06-84 DPU 13-161Algonquin 510804-R1 AFT-1 30,000 10,950,000 365 10/31/2031 10/31/2030 N Y N DPU 13-158 11/1/2016 15 years DPU 13-158Granite 22-001-FT-1 FT-1 12,000 1,812,000 151 10/31/2020 None N Y Y DPU 13-161 DPU 13-161Iroquois R182001 RTS-1 28,840 10,526,600 365 11/1/2022 11/1/2021 N Y Y 11/1/1993 20 years DPU 13-161 DPU 13-161

National Fuel N11117 FST 10,000 3,650,000 365 3/31/2021 3/31/2020 N Y Y 4/1/2008 5 yearsDPU 93-129; DTE 06-84; DPU 07-65; DPU 08-79; DPU 12-04 (5-year

renewal); DPU 13-161 DPU 17-166

PNGTS 208540 FT 16,000 5,840,000 365 10/31/2033 10/31/2032 N N NDPU 95-128 (approved) &

DTE 00-99 (prudence review) 5/1/2006 20 years DPU 13-161

PNGTS 208535 FT 45,500 16,607,500 365 10/31/2040 10/31/2039 N N NDPU 95-128 (approved) &

DTE 00-99 (prudence review) 5/1/2006 20 years DPU 13-161PNGTS PXP 12,675 1,913,925 151 N N N 11/1/2019

Texas Eastern 800462 CDS 36,369 13,274,685 365 10/31/2022 None N Y N 10/1/1994 18 yearsDTE 06-84; DPU 07-65; DPU 08-79; DPU 12-04 (5-year renewal); DPU 13-

161 DPU 17-166

Texas Eastern 800414 CDS 1,056 385,440 365 10/31/2027 10/31/2022 N Y N 9/1/1994 18 yearsDTE 06-84; DPU 07-65; DPU 08-79; DPU 12-04 (5-year renewal); DPU 13-

161 DPU 13-161Texas Eastern 800382 FT-1 4,235 1,545,775 365 10/31/2022 None N Y N 11/1/1993 16 DPU 08-79; DPU 12-04 (5-year renewal); DPU 13-161 DPU 17-166Tennessee 39741-FTATGP FT-A 4,081 1,489,565 365 3/31/2025 3/31/2024 N Y N DTE 02-52 1/15/2003 7 yrs 2 mos DTE 02-75; DTE 03-32; DTE 04-64; DTE 04-111; DTE 06-84; DPU 11-89 DPU 17-166Tennessee 5291-FTATGP FT-A 6,171 2,252,415 365 3/31/2025 3/31/2024 N Y N DTE 02-52 11/1/1993 9 yrs 2 mos DTE 02-75; DTE 03-32; DTE 04-64; DTE 04-111; DTE 06-84; DPU 11-89 DPU 17-166

Tennessee 5293-FTATGP FT-A 12,547 4,579,655 365 10/31/2024 10/31/2023 N Y N 11/1/1993 7 yearsDTE 00-52; DTE 02-75; DTE 03-32; DTE 06-84; DPU 07-65; DPU 08-79;

DPU 12-06; DPU 12-64; DPU 13-161 DPU 17-166Tennessee 5196-FTATGP FT-A 15,375 5,611,875 365 4/30/2045 4/30/2044 N Y N 11/1/1993 1yr 5 mos DTE 00-52; DTE 03-32; DTE 04-64; DTE 04-111; DTE 06-84; DPU 08-79 DPU 13-161

Tennessee 5173-FTATGP FT-A 12,748 4,653,020 365 10/31/2023 10/31/2022 N Y N 11/1/1993 7 yearsDTE 00-52; DTE 02-75; DTE 03-32; DTE 06-84; DPU 07-65; DPU 08-79;

DPU 10-65; DPU 11-89 DPU 17-166Tennessee 41098-FTATGP FT-A 18,733 6,837,545 365 10/31/2022 10/31/2021 N Y Y 11/1/2002 10 years DPU 10-65 DPU 13-161Tennessee 98775-FTAHTGP FT-A 6,100 2,226,500 365 10/31/2032 10/31/2030 N Y N DPU 10-49 11/1/2012 20 years DPU 13-161 DPU 13-161Tennessee 95349-FTATGP FT-A 9,774 3,567,510 365 10/31/2022 10/31/2021 N Y Y 11/1/2011 6 years DPU 13-161 DPU 13-161Tennessee 48427-FTATGP FT-A 17,000 6,205,000 365 10/31/2020 10/31/2019 N Y Y DTE 03-79 10/7/2005 10 years DTE 06-84 DPU 13-161Tennessee 48426-FTILTGP FT-IL 17,000 6,205,000 365 10/31/2020 10/31/2019 N Y Y DTE 03-79 9/26/2005 10 years DTE 06-84 DPU 13-161Tennessee 645-ITTGP IT 50,000 18,250,000 365 12/31/2049 None N Y N 11/1/1993 19 yearsTennessee 330904-FTATGP FT-A 56,000 20,440,000 365 10/31/2020 10/31/2019 N Y Y 11/1/2018TransCanada SH 41234 FT 26,062 9,512,630 365 10/31/2026 10/31/2025 N Y N DTE 05-48 11/1/2006 10 years DTE 06-84; DPU 13-161 DPU 17-166TransCanada MH 33321 FT 16,000 5,840,014 365 10/31/2026 10/31/2025 N Y N DTE 05-48 11/1/2007 1 yr 4 mos DPU 13-161 DPU 17-166

Transco 1006548 FT 1,254 457,710 365 3/31/2021 3/31/2020 N Y Y 11/1/1993 19yrs 6 mosDTE 06-84; DPU 07-65; DPU 08-79; DPU 12-04 (3-year renewal); DPU 13-

161 DPU 17-166Union Gas M12204 M12 26,352 9,618,480 365 10/31/2022 10/31/2020 N Y Y DTE 05-48 11/1/2010 7 years DTE 06-84 DPU 13-161Millennium 217524 FT-1 15,000 5,475,000 365 3/31/2034 3/31/2033 N Y N DPU 15-142 4/1/2019 15 years DPU 15-142

Underground Storage MDWQ Capacity

Dominion 600002 GSS-TE 14,758 1,441,753 151 3/31/2026 3/31/2024 N Y N 10/1/1993 6yrs 7 mos DPU 08-79; DPU 13-161 DPU 17-166

National Fuel O11116 FSS 10,000 1,100,000 151 3/31/2021 3/31/2020 N Y Y 4/1/2008 5 yearsDTE 06-84; DPU 07-65; DPU 08-79; DPU 12-04 (5-year renewal) ; DPU 13-

161 DPU 17-166

Texas Eastern 400502 FSS-1 1,056 63,360 151 4/30/2027 4/30/2022 N Y N 9/1/1994 17yrs 8mosDTE 06-84; DPU 07-65; DPU 08-79; DPU 12-04 (5-year renewal); DPU 13-

161 DPU 13-161Texas Eastern 400193 SS-1 22,819 1,588,950 151 4/30/2025 4/30/2020 N Y Y 9/1/1994 18yrs 8mos DPU 12-64; DPU 13-161 DPU 17-166

Tennessee 5178 FS-MA 19,755 1,222,594 151 10/31/2023 10/31/2022 N Y N 12/1/1994 5yrs 11mosDTE 00-52; DTE 02-75; DTE 03-32; DTE 06-84; DPU 07-65; DPU 08-79;

DPU 12-04 (5-year renewal); DPU 13-161 DPU 17-166Enbridge UTEC-CMA-3 USS 16,000 1,600,000 151 3/31/2022 9/30/2020 N Y Y DPU 15-175 6 years DPU 15-175Enbridge LST089 USS 26,500 1,820,000 69 3/31/2022 9/30/2020 N Y Y DPU 17-97 4 years DPU 17-97

TABLE G-24

/1: CMA has determined that the contracts for which the Company requests approval have (a) no material changes and (b) no reasonable alternatives.



Page A-59

Appendix 4: Summary of Demand Forecasting Framework

Customer Segment Forecast Daily Planning Load Model Purpose Forecast demand for gas on a quarterly

basis by division based on projected economic and demographic conditions

Forecast daily planning load shape by division based on weather conditions and appropriate indicator variables for day of the week, month and season.

Periodicity Quarterly Daily Units of Time Billing quarter/Billing month Gas Day, 10:00 am to 10:00 am Historical Time Period

1994 Q1 – 2018 Q4 (except Lawrence ended with 2018 Q2)

Recent year (365 days)

Independent Variables Types

Economic, demographic, and weather data

Weather, days of week, season, months

Demand Data Detail

4 customer segments and capacity exempt

Total division planning load

Demand Data Source

Internal Company monthly reports Internal company gate station and local LNG and LP production facility meter reads

Determination of Forecast Demand

Results from (1) number of customers model times (2) use per customer model equals demand.

Daily planning load shape model

Forecast Period 2019/20 – 2023/24 2019/20 Daily, with extrapolations through 2023/24

Planning Load Planning Load = total firm demand, minus capacity exempt demand

Planning Load modeled directly



Page A-60

Brockton Forecast

0

2000

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2010 2014 2018 2022

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omer

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Residential Non-Heat CustomersFour Quarter Average

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Residential Heat CustomersFour Quarter Average

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C&I Low-Load Factor CustomersFour Quarter Average

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C&I High-Load Factor CustomersFour Quarter Average

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2010 2014 2018 2022

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Residential Non-Heat Dth per CustomerFour Quarter Sum

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Residential Heat Dth per CustomerFour Quarter Sum

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C&I Low-Load Factor Dth per CustomerFour Quarter Sum

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1400

1600

1800

2010 2014 2018 2022

Dth/

Cust

omer

C&I High-Load Factor Dth per CustomerFour Quarter Sum

020000400006000080000

100000120000140000160000180000200000

2010 2014 2018 2022

Dth

Residential Non-Heat Dth Four Quarter Sum

0

2000000

4000000

6000000

8000000

10000000

12000000

14000000

16000000

2010 2014 2018 2022

Dth

Residential Heat Dth Four Quarter Sum

0

1000000

2000000

3000000

4000000

5000000

6000000

7000000

8000000

9000000

2010 2014 2018 2022

Dth

C&I Low-Load Factor Dth Four Quarter Sum

0500000

100000015000002000000250000030000003500000400000045000005000000

2010 2014 2018 2022

Dth

C&I High-Load Factor Dth Four Quarter Sum

0

500000

1000000

1500000

2000000

2500000

3000000

2010 2014 2018 2022

Dth

Capacity Exempt Dth Four Quarter Sum



Page A-61

Brockton History

0

2000

4000

6000

8000

10000

12000

2010 2014 2018

Cust

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s

Residential Non-Heat CustomersQuarterly

105000

110000

115000

120000

125000

130000

135000

140000

145000

2010 2014 2018

Cust

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s

Residential Heat CustomersQuarterly

0

2000

4000

6000

8000

10000

12000

14000

16000

2010 2014 2018

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s

C&I Low-Load Factor CustomersQuarterly

0

500

1000

1500

2000

2500

3000

3500

2010 2014 2018

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s

C&I High-Load Factor CustomersQuarterly

0

1

2

3

4

5

6

7

8

2010 2014 2018

Dth/

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omer

Residential Non-Heat Dth per CustomerQuarterly

0

10

20

30

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70

2010 2014 2018

Dth/

Cust

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Residential Heat Dth per CustomerQuarterly

0

50

100

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200

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400

2010 2014 2018

Dth/

Cust

omer

C&I Low-Load Factor Dth per CustomerQuarterly

0

100

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600

2010 2014 2018

Dth/

Cust

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C&I High-Load Factor Dth per CustomerQuarterly

0

10000

20000

30000

40000

50000

60000

70000

2010 2014 2018

Dth

Residential Non-Heat Dth Quaterly

0

1000000

2000000

3000000

4000000

5000000

6000000

7000000

8000000

2010 2014 2018

Dth

Residential Heat Dth Quarterly

0

1000000

2000000

3000000

4000000

5000000

6000000

2010 2014 2018

Dth

C&I Low-Load Factor Dth Quarterly

0

200000

400000

600000

800000

1000000

1200000

1400000

1600000

2010 2014 2018

Dth

C&I High-Load Factor Dth Quarterly

0

200000

400000

600000

800000

1000000

1200000

2010 2014 2018

Dth

Capacity Exempt Dth Quarterly



Page A-62

Lawrence Forecast

0

500

1000

1500

2000

2500

3000

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4000

2010 2014 2018 2022

Cust

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s


34000

36000

38000

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2010 2014 2018 2022

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500

1000

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2010 2014 2018 2022

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1000

2010 2014 2018 2022

Dth/

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2010 2014 2018 2022

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2010 2014 2018 2022

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2010 2014 2018 2022

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2010 2014 2018 2022

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2010 2014 2018 2022

Dth




Page A-63

Lawrence History

0

500

1000

1500

2000

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3000

3500

4000

2010 2014 2018

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35000

36000

37000

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39000

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2010 2014 2018

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Page A-64

Springfield Forecast

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0100002000030000400005000060000700008000090000

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2010 2014 2018 2022

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Page A-65

Springfield History

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2000

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2010 2014 2018

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7000072000740007600078000800008200084000860008800090000

2010 2014 2018

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0100020003000400050006000700080009000

2010 2014 2018

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2010 2014 2018

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2010 2014 2018

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2010 2014 2018

Dth




Page A-66

Appendix 6: Calculation of Billing Cycle EDD Variable

Because a significant portion of gas use is related to space heating in homes and businesses, demand for natural gas is strongly affected by cold weather. Studies have shown that demand for gas is related to a measure of cold weather, Effective Degree Days (“EDD”), which accounts for temperature and wind speed. EDDs are calculated as the positive difference between (a) a base temperature1, and (b) the actual daily temperature, with a factor to account for wind speed.

It is common operating practice for gas distribution companies, including CMA, to measure and record gas usage data in “billing months”. For that purpose, customers are divided into groups, or billing cycles2, and each group of billing cycle customers is processed through the Company’s billing procedures in succeeding business days throughout the month; distribution companies generally have approximately 20 billing cycles. Because the billing cycle schedules are set to accommodate weekends and holidays, customers in a billing cycle are read at approximately the same time of the month, every month.

As a result of this billing process, most of the gas consumption between meter readings of customers in an early billing cycle (e.g., Cycles 1 or 2) occurs in the prior calendar month; in contrast, most of the gas consumption between meter readings of customers in a later billing cycle (e.g., Cycles 19 or 20) occurs in the current calendar month. “Billing Month deliveries” are the gas deliveries as measured by meter readings and recorded by billing month (which includes consumption in the prior and current calendar month), and “Calendar Month deliveries” are estimated gas deliveries by calendar month.

Billing month and billing quarter EDD variables were created for CMA’s F&SP customer segment models to be consistent with the Company’s customer segment billing month data; the billing month EDDs were summed to billing quarters. Weather data from the station at Windsor Locks Connecticut was used for Springfield; Lawrence weather data values were determined by averaging weather data from Bedford, MA and Portsmouth, NH; and Brockton weather data values were determined by averaging weather data from Bedford, MA and Providence, RI. Billing month EDD data was derived from daily EDD by summing the days identified for each billing cycle and averaging the cycles.

1 The base temperature is the temperature at which customers start to turn on their space heating

equipment; the base temperature is typically 65 degrees. 2 Dividing the customers into billing cycles allows for the most efficient use of meter reading and

billing systems.



Page A-67

Calculation of Design Day, Prior Day and Design Winter EDD

The design day and design year standards represent extreme winter weather conditions that have a statistically defined probability of occurring on a very infrequent basis; the design day and design year standard is used to assess the Company’s plans to provide reliable service under extremely cold weather conditions. CMA’s design planning standards for design day and design winter are derived by applying a 1-in-33 probability of occurrence to a t-distribution. CMA’s design day standard was determined to be 78 EDDs for Brockton, 78 EDDs for Springfield and 80 EDDs for Lawrence, based on the t distribution of January EDDs for 1968 to 2019, and a 1-in-33 probability of occurrence.

The 1-in-33 level of heating season EDD was allocated to winter months based on normal year monthly EDD to derive Design year EDDs. The analysis is based on data for the winter beginning November 1967 through the winter beginning November 2018.

Design Winter EDD Brockton Lawrence Springfield Mean 5033 5264 5206 Std Dev 428.913 447.814 379.073 Design Winter EDD 5857 6124 5934

Winters ending March Year Brockton Lawrence Springfield 1968 5620 5872 5561 1969 5627 5879 5832 1970 5601 5704 5792 1971 5515 5684 5581 1972 5202 5449 5421 1973 4986 5321 5149 1974 4844 5110 5145 1975 4800 5025 5146 1976 4805 5010 5036 1977 5525 5724 5611 1978 5390 5488 5509 1979 5268 5402 5538 1980 4991 5196 5172 1981 5395 5649 5549 1982 5339 5532 5334 1983 4550 4775 4780 1984 5142 5366 5421 1985 4956 5142 5152 1986 5019 5221 5274 1987 5186 5425 5261 1988 5155 5286 5234 1989 4972 5076 5137



Page A-68

Winters ending March Year Brockton Lawrence Springfield 1990 5107 4147 5219 1991 4514 4750 4730 1992 4877 5169 5140 1993 5415 5676 5453 1994 5279 5632 5681 1995 4401 4742 4713 1996 5311 5567 5672 1997 4862 5123 5005 1998 4645 4925 4792 1999 4643 4890 4894 2000 4621 4921 4791 2001 3590 3898 5481 2002 4406 4660 4381 2003 5598 5919 5613 2004 5275 5528 5356 2005 5328 5622 5438 2006 4827 5126 4932 2007 4768 5116 4867 2008 5035 5417 5093 2009 5267 5550 5286 2010 4814 5064 4789 2011 5355 5595 5440 2012 4174 4448 4130 2013 5017 5297 5057 2014 5629 5951 5702 2015 5798 6096 5710 2016 4287 4580 4292 2017 4858 5151 4925 2018 5067 5409 5243 2019 5084 5420 5275



Page A-69

Normal Winter EDDs Brockton Lawrence Springfield

Jan 1224 1289 1288 Feb 1039 1096 1093 Mar 918 964 915 Apr 550 600 514 May 257 297 203 Jun 62 82 38 Jul 6 10 3

Aug 8 17 6 Sep 95 125 86 Oct 409 456 411 Nov 703 757 722 Dec 1055 1118 1080

Winter 4939 5224 5098 Design Winter EDDs Brockton Lawrence Springfield

Jan 1452 1511 1499 Feb 1232 1285 1272 Mar 1089 1130 1065 Apr 550 600 514 May 257 297 203 Jun 62 82 38 Jul 6 10 3

Aug 8 17 6 Sep 95 125 86 Oct 409 456 411 Nov 834 887 840 Dec 1251 1311 1257

Winter 5858 6124 5933



Page A-70

Appendix 8: Statistical Techniques and Glossary

Regression modeling techniques were used to generate the demand forecasts for the three divisions. The

regression analyses were developed in the SAS software package. Regression modeling techniques were

used to develop separate quarterly Brockton, Lawrence and Springfield forecasts of (a) number of

customers, (b) use per customer for Residential Heating, Residential Non-Heating, and C&I Low-Load

Factor (“LLF”) and High-Load Factor (“HLF”) customer segments, (c) total volume for capacity exempt

(“CE”) segments.

Regression Analysis

Econometrics is the empirical determination of economic laws: it involves the application of statistical

techniques and analyses to the study of economic data for the purpose of quantifying the relationships

implied by these laws. A fundamental statistical method of econometrics is regression analysis, which is

concerned with the study of the relationship between one variable, i.e., the dependent variable, and one or

more other variables, i.e., the independent or explanatory variables. One of the primary uses of regression

analysis is to forecast the values of the dependent variable, given forecast values of the independent

variables.5

CMA developed regression equations of (a) number of customers, (b) use per customer, and/or (c)

demand with appropriate variables including: effective degree days, natural gas prices, economic and

demographic data, and dummy and trend variables. Each of CMA’s forecast models explains historical

values of the dependent variable as a function of historical values of the independent variables; forecasts

of each model’s dependent variable are derived by combining the estimated model with the forecasted values of

the independent variables of the model.

The forecast models for this F&SP were developed using the following process: (a) a statement about the

expected behavior of customers in a particular class was developed; (b) appropriate data was collected; (c)

mathematical and statistical models were specified; (d) the model parameters were estimated; (e) the

accuracy of the model was checked; (f) hypotheses about the model and its parameters were tested; and

(g) the models were used to prepare the forecast.6

First, economic theory and standard utility forecasting practice were used to identify (a) variables that

could have an effect on the dependent variable – the independent variables - in each equation, and (b)

the expected sign of the coefficients for those variables. For example, the EDD variable is expected to

5 A glossary of statistical terms can be found at the end of this Appendix 8.

6 This process is a standard prescribed in many econometrics textbooks.



Page A-71

affect use per customer, and the EDD coefficient should be positive (i.e., when EDDs increase, demand

should increase, and similarly for decreases in EDDs). The price variable is also expected to affect use

per customer such that the price coefficient should be negative (i.e., when natural gas prices increase,

demand should decrease; similarly, demand is expected to rise in response to a decline in price).

For each of the models, after possible explanatory variables were identified and the data sets were

developed, regression equations were estimated to test various combinations of independent variables.

Based on: (1) the theoretical relevance and signs of the independent variables; (2) the results of various

statistical tests that assess the significance of the independent variables included in the equation; and (3)

the explanatory power of the equation as a whole, a preliminary regression equation was identified for

each model. If the sign of an independent variable was counter to expectations, either (a) that model was

not considered further or (b) modified forms of the model with different variables were considered. The

statistical significance of each independent variable was determined by examining the variable t-test

values. Variables that were significant at the 0.10 level were included in a model. Exceptions to this

practice were made for some coefficient estimates for variables that are believed to be relevant to the

explanation of the dependent variable and to accommodate the fact that the objective of forecasting – in contrast

with the objective of pure estimation - traditionally prescribes, and allows for, relaxed levels of statistical

significance for relevant variables. This practice improves scenario analysis and offers a superior alternative to the

practice of constraining less-than-significant coefficient estimates to be zero – that is, eliminating those variables

altogether. Finally, equations were evaluated based on explanatory power, as determined by the R2.

Models that met all of these criteria were subjected to further testing for autocorrelation,

heteroskedasticity, stability, multicollinearity, outliers, and ex post forecasting.

Autocorrelation

Statistical theory requires that, in order for the coefficient estimates to possess desirable properties of

unbiasedness and efficiency, the residuals (the “error terms”) associated with a regression equation be

independent of one another (i.e., there should be no relationship or correlation in the residuals over time).

Correlation of residuals over time is known as “autocorrelation”. If the error terms are autocorrelated, the

efficiency of ordinary least-squares (OLS) parameter estimates is adversely affected and standard error

estimates are biased. One aspect of time series analysis is to identify and correct for autocorrelation.

To diagnose autocorrelation, CMA evaluated each model observing, at a minimum, ACF/PACF graphs for

orders 1 through 8, where order refers to the lag between error terms. If autocorrelation was detected –

indicated by a ACF or PACF measures that were greater than two standard errors for any given order, an

autoregressive error correction procedure was used to re-estimate the model. The autoregressive error

model was selected through stepwise backward elimination of autoregressive terms.



Page A-72

The type of autocorrelation correction applied is specified by the order: an order of ‘0’ means that OLS

errors were not measured to be autocorrelated and no correction was applied; a correction order of ‘1’

incorporates a relation between the current error term and the error term lagged one period; a correction

order of ‘2’ incorporates a relation between the current error term and the error terms lagged both one and

two periods, and so on. Autoregressive terms to correct for autocorrelation were applied up through order

8 (i.e. error terms lagged up to 8 periods or quarters).

In Appendix 9, Detailed Model Statistics, the ACF and PACF graphs are provided for the final version for

each model.

Heteroskedasticity

The most common econometric model – an ordinary regression model – estimated by the method of

ordinary least squares (OLS) assumes that the errors produced by the estimation procedure have the same

variance throughout the sample used for estimation purposes. This is known as the homoskedasticity

property. However, when the error variance is shown to be not constant, the errors are characterized as

heteroskedastic. Heteroskedasticity can cause the OLS estimates to be inefficient and the OLS forecast

error variance to be inaccurate, since the predicted forecast variance is based on the average variance

instead of the variability at the end of the series.

To diagnose heteroskedasticity, CMA relied upon the Engle Lagrange multiplier (LM) test statistics and the

portmanteau (Q) test statistics, for which test statistics and significance p-values were reviewed for lag

windows 1 through 12 for each regression equation. Potential evidence of heteroskedasticity is revealed

when the probability value calculated for a particular lag has a value less than .001. The results of the

heteroskedasticity tests are provided in Appendix 9 for the final version of each model, with a total of 12

LM statistics and 12 Q statistics calculated for each model – a total of 24 measures.

Stability and Structural Change

The Chow test was used to test for break points or structural changes in each model. The Chow test is an

application of the F-test, which requires a portioning of the data into two parts and compares the sum of

squared errors from three regressions – one for each sample period before and after the specified

breakpoint and one for the pooled data. The Chow test was performed for each regression equation for

any break point suspected of being associated with a structural change. If any structural change was

determined to be statistically significant on the basis of the Chow test, shifts in either the intercept or a

particular slope coefficient associated with the structural change were incorporated into the model with

additional dummy variables and interaction terms.



Page A-73

Multicollinearity

A key assumption of a multiple regression model – a model with two or more explanatory, or

independent, variables – is that there exists no exact linear relationship among the independent variables.

In the case of an exact linear relationship – or perfect collinearity – estimation of the parameters of the

model is not possible. In practice, there is always some degree of less-than-exact collinearity among the

independent variables of a model. Any relationship among two or more independent variables has the

ability to undermine the statistical estimation procedure’s ability to reliably separate out the influence of

one independent variable on the dependent variable from the influence of any other, making any one

estimate of a collinear variable more unreliable – for example, producing wildly different parameter

estimates for changes in the historical sample used to estimate the regression - than it would be if

collinearity were not a problem. However, collinearity only raises the possibility of this estimation

problem. It does not conclusively demonstrate that the model estimates do not possess desirable

properties, and as will be discussed further in the section on stability of model estimates, an apparent

high degree of collinearity in the data does not necessarily undermine the estimated model’s ability to

produce reliable forecasts.

To test for multicollinearity, CMA calculated a correlation matrix among the causal variables for each

model. In Appendix 9, Detailed Model Statistics, the correlation matrices are provided for each model.

An additional conclusive test of the role and effect of varying degrees of collinearity is provided by the set

of procedures concerning Ex Post Forecasting, discussed below.

Outliers

Residual values are provided for each customer segment model in Appendix 9, which contains tables of

quarterly values of actuals, fitted values, residuals, variations, and standardized (studentized) residuals.

The values of the studentized residuals (SRES) express the residuals in terms of their deviations from

average values, providing a basis for identification of possible dummy variables to be included in the set

of explanatory variables in a model. An SRES of approximately 3.0 or higher indicates an outlier that is

outside the 99% confidence interval for a model’s fitted value.

These residuals are often associated with some unexplained, or unknown, data or billing processes that are

understood to be just 1-period – i.e., 1-quarter – in length. Despite the Company’s best efforts to

eliminate data peculiarities and anomalies, certain instances of data variations remain unexplained; CMA

will continue to be vigilant about improving the quality of its data base.

Structural breaks, or shifts in the underlying behavioral relationships specified for a customer segment,

can also contribute to the occurrence of significant residuals, not only for isolated single-period history

but for consecutive periods of time. Chow tests, described above, can be applied to the regression



Page A-74

estimates to determine where these shifts may be located. The technique employed in CMA’s models

to account for structural shifts is the specification of an interaction term constructed as the product of a

dummy variable and the explanatory variable associated with the shift in historical relationship. The

dummy variable simply distinguishes between the two periods of different character, and the interaction

term formed as the product of dummy and explanatory variables estimates a change in slope, or

elasticity.

Ex Post Forecast

The role of an ex post forecast is prominent in developing models that are designed for use as forecasting

tools. All of the issues described above – autocorrelation, heteroskedasticity, stability, and

multicollinearity – along with statistical significance and appropriate model specification, provide a

systematic focus on raising questions about the ability of any model to ultimately produce forecasts that

can be relied upon for business purposes. Review of ex post forecasts offers an evaluation of the models’

abilities to produce reliable forecasts by simulating how any model will actually predict values. For this

F&SP, ex post forecasts were performed by simulating the 2018 experience, using historical data only

through 2017 to re-estimate the same models built on historical data through 2017, and then deriving ex

post forecasts for 2018 from the re-estimated models. Due to the truncated data set, the ex post

forecast for Lawrence was performed by re-estimating the same models using data through

2017Q2, and simulating 2017Q3-2018Q2. CMA used the ex post analyses to evaluate model

accuracy and model stability. Model accuracy refers to the proximity of projected values to actual values

for the ‘ex post period’ specified – in this case, the year 2018. Model stability refers to variation in the

estimates of the coefficients of a model caused by estimating the model on a different set of data. Ex post

results are reported in Appendix 9 for each customer segment and division.



Page A-75

Glossary of Statistical Terms7

Term Definition Adjusted R2

A measure of the overall goodness of fit for the regression model, taking into account the number of independent variables in the model. Adjusted R2 ranges from 0 to 1; the closer the Adjusted R2 value is to 1, the better the fit of the model. Adjusted R2 can be interpreted as the amount of variability of the dependent variable that is explained by the regression equation, taking into consideration the number of independent variables in the model.

Autocorrelation A measure of the correlation of the values of a series with the values lagged by 1 or more cases. (Other equivalent terms include: serial correlation)

Autocorrelation Function (“ACF”)

A function defined as the autocorrelation of the residuals at various lags; can be shown as a graph.

Correlation A measure of the degree of relationship between two variables. The value of a correlation can range from -1 to 1, with values close to +/-1 indicating a strong relationship between two variables and a correlation close to 0 indicating no relationship between the variables.

Dependent Variable A dependent variable is one that is observed to change in response to the independent variables. (Other equivalent terms include: response variable, result variable, outcome variable, endogenous variable, output variable, Y- variable)

Estimate (of the Independent Variable)

A measure of the value of the model parameter (i.e., independent variable). (Other equivalent terms include: coefficient of the independent variable)

F statistic A measure of whether a regression equation is significant (i.e., whether the set of independent variables in a model explains a significant portion of the variability of the dependent variable). Calculated as the mean-square regression divided by the mean square residuals. The value of the F statistic ranges from zero to positive infinity, with large positive values indicating that the model is significant.

Forecast The values predicted by the model for the forecast period. Independent Variable A variable used to attempt to explain the behavior of another variable (see

Dependent Variable) in a regression equation. (Other equivalent terms include: explanatory variable, exogenous variable, external variable, predictor variable, causal variable, input variable, X-variable, regressors)

Model A specific set of independent variables and their parameters used to explain a dependent variable. (Other equivalent terms include: Equation)

7 These terms are defined as they relate to the econometric/regression analysis used in this F&SP.



Page A-76

Term Definition Number of Observations (“N”)

The amount of data used to develop the model (i.e., the number of data points that are included for each variable in the model).

Number of Predictors The amount of independent variables included in the model. Note that Number of Predictors measures the total number of independent variables included in the model, not only the significant independent variables.

Partial Autocorrelation Function (“PACF”)

A function defined as the partial autocorrelation of the residuals at various lags. Partial autocorrelation is a measure of the correlation of the values of a series with values lagged by one or more cases, after the effects of correlations at the intervening lags have been removed; can be shown as a graph.

R2 A measure of the overall goodness of fit for the regression model. R2 ranges from 0 to 1; the closer the R2 value is to 1, the better the fit of the model. R2

can be interpreted as the amount of variability of the dependent variable that is explained by the regression equation.

Residual The difference between the actual historical values of the dependent variable and the values predicted by the model (i.e., the model fits). (Other equivalent terms include: error, error term)

Root Mean Square Error (“RMSE”)

A measure of the variability of the residuals. (Other equivalent terms include: Standard Error of the Regression)

Significance of the t statistic

A measure of the strength (or significance level) of the t statistic. A low value of the significance level of the t statistic is desired, as it indicates the related independent variable is significant in the equation. In general, only independent variables that had t statistics that were significant at the 0.10 level (i.e. less than 0.10) were included in the final equation. (Other equivalent terms include: p-value) Although statistical significance is dependent on the number of observations and number of explanatory variables in the equation, generally, t statistics greater than 2.0 are statistically significant.

Standard Error (of the Estimate of the Independent Variable) (“SE”)

A measure of how much the value of a test statistic varies (i.e., the standard deviation of the sampling distribution for a statistic), in this case the Estimate of the Independent Variable.

t statistic A measure of whether the coefficient for an independent variable is statistically different than zero. Calculated as the Estimate of the Independent Variable divided by its Standard Error. The value of t statistic ranges from negative infinity to positive infinity, with values far from zero indicating that the independent variable is significant in the model. (Other equivalent terms include: t-Statistic, t-Test, Student’s t)



Page A-77

Multicollinearity Test

The CORR Procedure

03:16 Monday, October 28, 2019 1

Pearson Correlation Coefficients Prob > |r| under H0: Rho=0

Number of Observations

CUST POPQ POPQxbefore2012 before2012 Q1 Q2 Q4

CUST 1.00000

48

0.98954 <.0001

48

-0.87019 <.0001

48

-0.87375 <.0001

48

0.00998 0.9463

48

-0.03163 0.8310

48

0.08203 0.5794

48

POPQ 0.98954 <.0001

48

1.00000

96

-0.79417 <.0001

96

-0.79578 <.0001

96

-0.03096 0.7646

96

-0.01031 0.9206

96

0.03094 0.7647

96

POPQxbefore2012 -0.87019 <.0001

48

-0.79417 <.0001

96

1.00000

96

0.99995 <.0001

96

-0.00062 0.9952

96

-0.00022 0.9983

96

0.00064 0.9951

96

before2012 -0.87375 <.0001

48

-0.79578 <.0001

96

0.99995 <.0001

96

1.00000

96

0.00000 1.0000

96

0.00000 1.0000

96

0.00000 1.0000

96

Q1 0.00998 0.9463

48

-0.03096 0.7646

96

-0.00062 0.9952

96

0.00000 1.0000

96

1.00000

96

-0.33333 0.0009

96

-0.33333 0.0009

96

Q2 -0.03163 0.8310

48

-0.01031 0.9206

96

-0.00022 0.9983

96

0.00000 1.0000

96

-0.33333 0.0009

96

1.00000

96

-0.33333 0.0009

96

Q4 0.08203 0.5794

48

0.03094 0.7647

96

0.00064 0.9951

96

0.00000 1.0000

96

-0.33333 0.0009

96

-0.33333 0.0009

96

1.00000

96



Page A-78

Brockton Residential Heating Customer Count Full Model

The AUTOREG Procedure


Dependent Variable CUST

Structural Change Test

Test Break Point Num DF Den DF F Value Pr > F

Chow 20 7 34 2.10 0.0706

Backward Elimination of Autoregressive Terms

Lag Estimate t Value Pr > |t|

6 0.068095 0.29 0.7730

3 -0.076550 -0.33 0.7412

4 0.089563 0.61 0.5467

7 0.103249 0.74 0.4664

Estimates of Autoregressive Parameters

Lag Coefficient Standard

Error t Value

1 -0.785991 0.141123 -5.57

2 0.504442 0.132146 3.82

5 0.306344 0.102572 2.99

8 0.301088 0.113912 2.64



Page A-79




Maximum Likelihood Estimates

SSE 1038720.78 DFE 37

MSE 28074 Root MSE 167.55159

SBC 662.580191 AIC 641.99698

MAE 117.965818 AICC 649.330314

MAPE 0.09549721 HQC 649.775406

Log Likelihood -309.99849 Transformed Regression R-Square 0.9998

Total R-Square 0.9997

Observations 48

Tests for ARCH Disturbances Based on Residuals

Order Q Pr > Q LM Pr > LM

1 1.8756 0.1708 0.9214 0.3371

2 3.6447 0.1616 1.8788 0.3909

3 5.9431 0.1144 2.4768 0.4795

4 12.3105 0.0152 5.0531 0.2819

5 12.3105 0.0308 5.1089 0.4027

6 13.2578 0.0391 6.8682 0.3332

7 13.5749 0.0593 7.2286 0.4055

8 13.7175 0.0894 7.9567 0.4377

9 20.0550 0.0176 13.3700 0.1466

10 23.3390 0.0096 13.6597 0.1891

11 32.4409 0.0006 14.2800 0.2179

12 35.3255 0.0004 14.3056 0.2816



Page A-80




Parameter Estimates

Variable DF Estimate Standard

Error t Value Approx Pr > |t|

Intercept 1 -431779 4032 -107.09 <.0001

POPQ 1 318.5717 2.2833 139.52 <.0001

POPQxbefore2012 1 -114.4465 2.7307 -41.91 <.0001

before2012 1 198865 4763 41.75 <.0001

Q1 1 2054 75.6302 27.16 <.0001

Q2 1 959.9228 57.6135 16.66 <.0001

Q4 1 1399 57.2419 24.44 <.0001

AR1 1 -0.6644 0.1278 -5.20 <.0001

AR2 1 0.5261 0.1212 4.34 0.0001

AR5 1 0.3823 0.0909 4.20 0.0002

AR8 1 0.4472 0.1127 3.97 0.0003



Page A-81






Page A-82

FULL MODEL FORECAST


Obs PRED RES YEAR Q CUST DIFF PCTDIFF SRES

1 114080.34 156.247 2007 1 114370.3333 289.997 0.002535601 0.93253

2 113453.36 55.576 2007 2 113535.3333 81.973 0.000722001 0.33170

3 112813.42 -220.231 2007 3 112515.6667 -297.756 -.002646352 -1.31441

4 114283.62 109.833 2007 4 114431 147.382 0.001287951 0.65552

5 115679.95 -221.757 2008 1 115401.3333 -278.620 -.002414358 -1.32351

6 114706.91 -134.165 2008 2 114545.3333 -161.578 -.001410600 -0.80074

7 114106.46 -113.601 2008 3 113969.6667 -136.792 -.001200253 -0.67800

8 116124.93 122.601 2008 4 116262 137.072 0.001178989 0.73172

9 117429.31 55.022 2009 1 117484.3333 55.022 0.000468339 0.32839

10 116624.51 209.821 2009 2 116834.3333 209.821 0.001795889 1.25228

11 116402.37 161.629 2009 3 116564 161.629 0.001386612 0.96465

12 118434.63 -12.961 2009 4 118421.6667 -12.961 -.000109446 -0.07735

13 119353.11 77.886 2010 1 119431 77.886 0.000652142 0.46485

14 118771.84 -194.507 2010 2 118577.3333 -194.507 -.001640338 -1.16088

15 118152.78 -238.779 2010 3 117914 -238.779 -.002025026 -1.42511

16 119730.06 41.602 2010 4 119771.6667 41.602 0.000347342 0.24829

17 120880.70 54.635 2011 1 120935.3333 54.635 0.000451773 0.32608

18 120296.82 -23.821 2011 2 120273 -23.821 -.000198055 -0.14217

19 119759.97 -41.968 2011 3 119718 -41.968 -.000350555 -0.25048

20 121677.34 -320.335 2011 4 121357 -320.335 -.002639611 -1.91186

21 122717.07 -52.735 2012 1 122664.3333 -52.735 -.000429916 -0.31474

22 122399.27 -164.270 2012 2 122235 -164.270 -.001343887 -0.98041

23 122041.92 373.418 2012 3 122415.3333 373.418 0.003050417 2.22867

24 124250.62 -8.617 2012 4 124242 -8.617 -.000069359 -0.05143

25 125563.89 -78.219 2013 1 125485.6667 -78.219 -.000623334 -0.46684

26 124943.21 72.453 2013 2 125015.6667 72.453 0.000579548 0.43242

27 124770.59 -9.252 2013 3 124761.3333 -9.252 -.000074159 -0.05522

28 126805.49 81.173 2013 4 126886.6667 81.173 0.000639726 0.48446

29 128160.84 174.156 2014 1 128335 174.156 0.001357041 1.03942

30 127919.46 -264.123 2014 2 127655.3333 -264.123 -.002069034 -1.57637

31 127124.11 39.219 2014 3 127163.3333 39.219 0.000308418 0.23407

32 129230.68 237.318 2014 4 129468 237.318 0.001833022 1.41639

33 130973.31 -82.977 2015 1 130890.3333 -82.977 -.000633946 -0.49523

34 130471.71 -1.045 2015 2 130470.6667 -1.045 -.000008007 -0.00624

35 130163.41 -223.406 2015 3 129940 -223.406 -.001719301 -1.33336



Page A-83

FULL MODEL FORECAST



36 132018.03 77.972 2015 4 132096 77.972 0.000590266 0.46536

37 133246.08 43.250 2016 1 133289.3333 43.250 0.000324481 0.25813

38 132620.30 81.036 2016 2 132701.3333 81.036 0.000610662 0.48365

39 132365.36 207.642 2016 3 132573 207.642 0.001566244 1.23927

40 134527.21 -191.874 2016 4 134335.3333 -191.874 -.001428322 -1.14516

41 135427.92 -90.924 2017 1 135337 -90.924 -.000671837 -0.54267

42 134990.20 133.131 2017 2 135123.3333 133.131 0.000985256 0.79457

43 135050.54 0.791 2017 3 135051.3333 0.791 0.000005857 0.00472

44 137003.78 83.891 2017 4 137087.6667 83.891 0.000611952 0.50069

45 138334.70 -54.362 2018 1 138280.3333 -54.362 -.000393130 -0.32445

46 137976.31 88.356 2018 2 138064.6667 88.356 0.000639959 0.52733

47 137693.83 2.833 2018 3 137696.6667 2.833 0.000020573 0.01691

48 139621.61 -176.939 2018 4 139444.6667 -176.939 -.001268881 -1.05603



Page A-84

Brockton Residential Heating Customer Count Ex Post Model






Page A-85

EX POST FORECAST STABILITY


Obs YEAR Q CUST XPRED EXDIFF EXPCTDIFF

1 2018 1 138280.3333 138275.21 5.126 0.000037073

2 2018 2 138064.6667 137885.49 179.177 0.001297777

3 2018 3 137696.6667 137803.60 -106.930 -.000776565

4 2018 4 139444.6667 139924.59 -479.921 -.003441656



Page A-86


03:16 Monday, October 28, 2019 10

Obs _NAME_ _LABEL_ EST1 XPEST1 PCTDIFF DIFF

1 CUST Parameter Estimate for CUST -1.00 -1.00 .00% 0.00

2 Intercept Intercept Parameter -431779.10 -433484.58 (.39%) 1705.48

3 POPQ Parameter Estimate for POPQ 318.57 319.53 (.30%) -0.96

4 POPQxbefore2012 Parameter Estimate for POPQxbefore2012 -114.45 -114.65 (.18%) 0.20

5 Q1 Parameter Estimate for Q1 2053.84 2073.33 (.94%) -19.49

6 Q2 Parameter Estimate for Q2 959.92 917.57 4.6% 42.35

7 Q4 Parameter Estimate for Q4 1399.07 1462.09 (4.3%) -63.02

8 _A_1 Parameter Estimate for _A_1 -0.66 -0.90 ( 26%) 0.23

9 _A_2 Parameter Estimate for _A_2 0.53 0.50 5.8% 0.03

10 _A_3 Parameter Estimate for _A_3 . . . .


12 _A_5 Parameter Estimate for _A_5 0.38 . . .

13 _A_6 Parameter Estimate for _A_6 . 0.31 . .



16 _LIKLHD_ Log-Likelihood -310.00 -289.15 7.2% -20.85

17 _MSE_ Estimate of Variance 28073.53 36591.42 ( 23%) -8517.88

18 _SSE_ Sum of Squares Error 1038720.78 1244108.14 ( 17%) -205387.36

19 before2012 Parameter Estimate for before2012 198865.46 199261.68 (.20%) -396.22



Page A-87

The CORR Procedure




RHUPC BMEDDxQ1Q2Q4 ARHHPxQ1Q2Q4

RHUPC 1.00000

64

0.98533 <.0001

64

0.63341 <.0001

64

BMEDDxQ1Q2Q4 0.98533 <.0001

64

1.00000

112

0.73227 <.0001

112

ARHHPxQ1Q2Q4 0.63341 <.0001

64

0.73227 <.0001

112

1.00000

112

BMEDDxy2008Q2andbefore 0.50835 <.0001

64

0.32155 0.0005

112

0.35672 0.0001

112

year2009Q3 -0.13854 0.2749

64

-0.12421 0.1919

112

-0.15836 0.0954

112



BMEDDxy2008Q2andbefore year2009Q3

RHUPC 0.50835 <.0001

64

-0.13854 0.2749

64

BMEDDxQ1Q2Q4 0.32155 0.0005

112

-0.12421 0.1919

112

ARHHPxQ1Q2Q4 0.35672 0.0001

112

-0.15836 0.0954

112

BMEDDxy2008Q2andbefore 1.00000

112

-0.03680 0.7001

112

year2009Q3 -0.03680 0.7001

112

1.00000

112



Page A-88

Brockton Residential Heat UPC Full Model



Dependent Variable RHUPC



Chow 20 5 54 0.93 0.4666



8 0.056996 0.41 0.6852

6 0.057144 0.39 0.6963

7 -0.095477 -0.74 0.4636

3 0.044925 0.41 0.6799

2 0.173044 1.70 0.0943



Error t Value

1 -0.328288 0.125693 -2.61

4 -0.679330 0.097378 -6.98

5 0.313929 0.126875 2.47



Page A-89





SSE 3.63604554 DFE 56

MSE 0.06493 Root MSE 0.25481

SBC 41.0046809 AIC 23.7336162

MAE 0.18559765 AICC 26.351798

MAPE 3.0725029 HQC 30.537561



Observations 64



1 0.3224 0.5702 0.2399 0.6243

2 4.4128 0.1101 2.6041 0.2720

3 4.5492 0.2079 2.8737 0.4115

4 7.5311 0.1103 3.7329 0.4434

5 7.6179 0.1786 3.8583 0.5700

6 9.8574 0.1308 4.5622 0.6011

7 11.7804 0.1080 5.9311 0.5478

8 11.7971 0.1605 6.5037 0.5910

9 11.8079 0.2244 6.5326 0.6857

10 11.8720 0.2937 6.5944 0.7631

11 13.3791 0.2693 11.0739 0.4371

12 13.6659 0.3225 11.7900 0.4627



Page A-90




Parameter Estimates



Intercept 1 1.2908 0.4422 2.92 0.0051

BMEDDxQ1Q2Q4 1 0.004790 0.000139 34.37 <.0001

ARHHPxQ1Q2Q4 1 -0.0457 0.0214 -2.14 0.0366

BMEDDxy2008Q2andbefore 1 0.000231 0.0000834 2.77 0.0076

year2009Q3 1 0.4044 0.1737 2.33 0.0235

AR1 1 -0.4062 0.1235 -3.29 0.0017

AR4 1 -0.9439 0.0367 -25.72 <.0001

AR5 1 0.4239 0.1249 3.39 0.0013



Page A-91






Page A-92

FULL MODEL FORECAST


Obs PRED RES YEAR Q RHUPC DIFF PCTDIFF SRES

1 19.8825 0.35201 2003 1 21.03361617 1.15115 0.05473 1.38147

2 8.1468 0.06417 2003 2 8.356173598 0.20939 0.02506 0.25181

3 1.3300 0.32835 2003 3 2.401520415 1.07150 0.44618 1.28859

4 9.5689 -0.13724 2003 4 9.1218752 -0.44703 -0.04901 -0.53859

5 19.5444 0.35247 2004 1 19.93353739 0.38917 0.01952 1.38325

6 7.7269 0.00170 2004 2 7.728575456 0.00170 0.00022 0.00667

7 2.3752 0.03805 2004 3 2.413288076 0.03805 0.01577 0.14933

8 9.2074 -0.44331 2004 4 8.764132607 -0.44331 -0.05058 -1.73975

9 19.4989 -0.66828 2005 1 18.8306301 -0.66828 -0.03549 -2.62262

10 8.0560 0.01868 2005 2 8.074658445 0.01868 0.00231 0.07330

11 2.1762 0.10787 2005 3 2.284089318 0.10787 0.04723 0.42332

12 7.8753 0.17011 2005 4 8.045368059 0.17011 0.02114 0.66759

13 17.1440 -0.39174 2006 1 16.75224055 -0.39174 -0.02338 -1.53737

14 6.5932 0.15383 2006 2 6.747070347 0.15383 0.02280 0.60371

15 2.2373 -0.03215 2006 3 2.205158103 -0.03215 -0.01458 -0.12619

16 7.0356 -0.25602 2006 4 6.779602848 -0.25602 -0.03776 -1.00475

17 17.6141 -0.01236 2007 1 17.60175279 -0.01236 -0.00070 -0.04852

18 7.0751 0.36178 2007 2 7.436927124 0.36178 0.04865 1.41979

19 2.2817 -0.05760 2007 3 2.2240962 -0.05760 -0.02590 -0.22605

20 8.0632 0.06505 2007 4 8.128219917 0.06505 0.00800 0.25529

21 17.0017 -0.21400 2008 1 16.78769165 -0.21400 -0.01275 -0.83984

22 7.1368 -0.13551 2008 2 7.001294975 -0.13551 -0.01935 -0.53179

23 2.0727 0.06283 2008 3 2.135509741 0.06283 0.02942 0.24659

24 8.1584 -0.16918 2008 4 7.989265624 -0.16918 -0.02118 -0.66392

25 17.6412 0.16180 2009 1 17.80301487 0.16180 0.00909 0.63497

26 6.5286 -0.34994 2009 2 6.178623293 -0.34994 -0.05664 -1.37332

27 2.3529 -0.10534 2009 3 2.247572149 -0.10534 -0.04687 -0.41338

28 7.4775 -0.25128 2009 4 7.226264901 -0.25128 -0.03477 -0.98613

29 16.6649 0.30257 2010 1 16.96747355 0.30257 0.01783 1.18740

30 5.1054 0.31396 2010 2 5.419315665 0.31396 0.05793 1.23211

31 1.9800 0.02934 2010 3 2.009317525 0.02934 0.01460 0.11515

32 7.8874 0.07305 2010 4 7.960410782 0.07305 0.00918 0.28668

33 18.1009 -0.56135 2011 1 17.53957487 -0.56135 -0.03200 -2.20299

34 6.8627 -0.28546 2011 2 6.5772257 -0.28546 -0.04340 -1.12028

35 1.7677 0.28194 2011 3 2.049633305 0.28194 0.13756 1.10648



Page A-93

FULL MODEL FORECAST



36 6.5161 -0.09251 2011 4 6.423598694 -0.09251 -0.01440 -0.36306

37 14.3776 -0.19766 2012 1 14.17991918 -0.19766 -0.01394 -0.77569

38 5.1569 -0.08008 2012 2 5.076860146 -0.08008 -0.01577 -0.31429

39 1.9606 -0.03476 2012 3 1.9258399 -0.03476 -0.01805 -0.13641

40 7.4668 -0.23767 2012 4 7.229125417 -0.23767 -0.03288 -0.93272

41 16.1546 -0.03254 2013 1 16.12201659 -0.03254 -0.00202 -0.12772

42 6.6679 -0.27967 2013 2 6.388266004 -0.27967 -0.04378 -1.09756

43 1.7840 0.22470 2013 3 2.008653856 0.22470 0.11187 0.88182

44 8.1663 -0.17976 2013 4 7.986526034 -0.17976 -0.02251 -0.70547

45 18.1719 0.08475 2014 1 18.25665381 0.08475 0.00464 0.33262

46 6.6027 0.29334 2014 2 6.896058658 0.29334 0.04254 1.15119

47 2.1383 -0.16876 2014 3 1.969561456 -0.16876 -0.08568 -0.66228

48 7.3218 -0.17362 2014 4 7.148221954 -0.17362 -0.02429 -0.68138

49 19.4427 -0.14257 2015 1 19.30011129 -0.14257 -0.00739 -0.55952

50 6.6547 0.16455 2015 2 6.819244172 0.16455 0.02413 0.64576

51 1.9918 -0.04382 2015 3 1.947950336 -0.04382 -0.02249 -0.17196

52 6.0955 -0.00319 2015 4 6.092299035 -0.00319 -0.00052 -0.01253

53 14.6681 -0.44609 2016 1 14.22200326 -0.44609 -0.03137 -1.75065

54 6.5144 -0.05920 2016 2 6.45516247 -0.05920 -0.00917 -0.23235

55 1.8401 0.02296 2016 3 1.863084238 0.02296 0.01232 0.09011

56 7.2100 -0.20662 2016 4 7.003347345 -0.20662 -0.02950 -0.81088

57 14.8786 0.21365 2017 1 15.0922709 0.21365 0.01416 0.83845

58 6.9773 0.03125 2017 2 7.008535412 0.03125 0.00446 0.12263

59 1.9033 0.04018 2017 3 1.943458537 0.04018 0.02067 0.15767

60 6.3629 0.57451 2017 4 6.937460943 0.57451 0.08281 2.25465

61 16.4133 0.09013 2018 1 16.50341456 0.09013 0.00546 0.35373

62 6.9866 0.08429 2018 2 7.070882244 0.08429 0.01192 0.33080

63 2.0282 -0.27324 2018 3 1.755012225 -0.27324 -0.15569 -1.07230

64 8.4398 -0.09186 2018 4 8.347934904 -0.09186 -0.01100 -0.36050



Page A-94

Brockton Residential Heat UPC Ex Post Model






Page A-95



Obs YEAR Q RHUPC XPRED EXDIFF EXPCTDIFF

1 2018 1 16.50341456 16.4474 0.05604 0.00340

2 2018 2 7.070882244 6.9702 0.10068 0.01424

3 2018 3 1.755012225 2.0476 -0.29261 -0.16673

4 2018 4 8.347934904 8.5527 -0.20476 -0.02453



Page A-96


03:18 Monday, October 28, 2019 10


1 ARHHPxQ1Q2Q4 Parameter Estimate for ARHHPxQ1Q2Q4 -0.04574 -0.06018 ( 24%) 0.01444

2 BMEDDxQ1Q2Q4 Parameter Estimate for BMEDDxQ1Q2Q4 0.00479 0.00483 (.76%) -0.00004

3 BMEDDxy2008Q2andbefore Parameter Estimate for BMEDDxy2008Q2andbefore 0.00023 -0.00002 (2E3%) 0.00025

4 Intercept Intercept Parameter 1.29079 1.66100 ( 22%) -0.37021

5 RHUPC Parameter Estimate for RHUPC -1.00000 -1.00000 .00% 0.00000




9 _A_4 Parameter Estimate for _A_4 -0.94393 -0.95478 (1.1%) 0.01085

10 _A_5 Parameter Estimate for _A_5 0.42395 0.51669 ( 18%) -0.09275




14 _LIKLHD_ Log-Likelihood -3.86681 -8.38539 ( 54%) 4.51858


16 _SSE_ Sum of Squares Error 3.63605 3.91671 (7.2%) -0.28067

17 year2009Q3 Parameter Estimate for year2009Q3 0.40436 0.49256 ( 18%) -0.08820



Page A-97

The CORR Procedure




TREND Q12015after TrendxQ12015after Q1 Q2 Q4 Q1xQ15A

TREND 1.00000

84

0.73776 <.0001

84

0.96739 <.0001

84

-0.03572 0.7470

84

-0.01191 0.9144

84

0.03572 0.7470

84

0.17005 0.1220

84

Q12015after 0.73776 <.0001

84

1.00000

84

0.87523 <.0001

84

0.00000 1.0000

84

0.00000 1.0000

84

0.00000 1.0000

84

0.27116 0.0126

84

TrendxQ12015after 0.96739 <.0001

84

0.87523 <.0001

84

1.00000

84

-0.01979 0.8582

84

-0.00660 0.9525

84

0.01979 0.8582

84

0.21550 0.0490

84

Q1 -0.03572 0.7470

84

0.00000 1.0000

84

-0.01979 0.8582

84

1.00000

84

-0.33333 0.0019

84

-0.33333 0.0019

84

0.84017 <.0001

84

Q2 -0.01191 0.9144

84

0.00000 1.0000

84

-0.00660 0.9525

84

-0.33333 0.0019

84

1.00000

84

-0.33333 0.0019

84

-0.28006 0.0099

84

Q4 0.03572 0.7470

84

0.00000 1.0000

84

0.01979 0.8582

84

-0.33333 0.0019

84

-0.33333 0.0019

84

1.00000

84

-0.28006 0.0099

84

Q1xQ15A 0.17005 0.1220

84

0.27116 0.0126

84

0.21550 0.0490

84

0.84017 <.0001

84

-0.28006 0.0099

84

-0.28006 0.0099

84

1.00000

84

Q2xQ15A 0.19005 0.0834

84

0.27116 0.0126

84

0.23005 0.0353

84

-0.28006 0.0099

84

0.84017 <.0001

84

-0.28006 0.0099

84

-0.23529 0.0312

84

TrendxD2010 -0.36755 0.0006

84

-0.39924 0.0002

84

-0.34942 0.0011

84

-0.01045 0.9249

84

-0.00348 0.9749

84

0.01045 0.9249

84

-0.10826 0.3270

84

TrendxQ12017A 0.95780 <.0001

84

0.73750 <.0001

84

0.94639 <.0001

84

-0.01576 0.8869

84

-0.00525 0.9622

84

0.01576 0.8869

84

0.18260 0.0964

84

DQ2Q42010 -0.25442 0.0195

84

-0.27937 0.0101

84

-0.24451 0.0250

84

-0.09017 0.4147

84

0.09017 0.4147

84

0.09017 0.4147

84

-0.07576 0.4934

84

D2011 -0.33199 0.0020

84

-0.40000 0.0002

84

-0.35009 0.0011

84

0.00000 1.0000

84

0.00000 1.0000

84

0.00000 1.0000

84

-0.10847 0.3260

84

CUST -0.98810 <.0001

36

-0.88155 <.0001

36

-0.89567 <.0001

36

0.03639 0.8331

36

0.00704 0.9675

36

-0.03525 0.8383

36

-0.32619 0.0522

36



Page A-98

The CORR Procedure




Q2xQ15A TrendxD2010 TrendxQ12017A DQ2Q42010 D2011 CUST

TREND 0.19005 0.0834

84

-0.36755 0.0006

84

0.95780 <.0001

84

-0.25442 0.0195

84

-0.33199 0.0020

84

-0.98810 <.0001

36

Q12015after 0.27116 0.0126

84

-0.39924 0.0002

84

0.73750 <.0001

84

-0.27937 0.0101

84

-0.40000 0.0002

84

-0.88155 <.0001

36

TrendxQ12015after 0.23005 0.0353

84

-0.34942 0.0011

84

0.94639 <.0001

84

-0.24451 0.0250

84

-0.35009 0.0011

84

-0.89567 <.0001

36

Q1 -0.28006 0.0099

84

-0.01045 0.9249

84

-0.01576 0.8869

84

-0.09017 0.4147

84

0.00000 1.0000

84

0.03639 0.8331

36

Q2 0.84017 <.0001

84

-0.00348 0.9749

84

-0.00525 0.9622

84

0.09017 0.4147

84

0.00000 1.0000

84

0.00704 0.9675

36

Q4 -0.28006 0.0099

84

0.01045 0.9249

84

0.01576 0.8869

84

0.09017 0.4147

84

0.00000 1.0000

84

-0.03525 0.8383

36

Q1xQ15A -0.23529 0.0312

84

-0.10826 0.3270

84

0.18260 0.0964

84

-0.07576 0.4934

84

-0.10847 0.3260

84

-0.32619 0.0522

36

Q2xQ15A 1.00000

84

-0.10826 0.3270

84

0.19419 0.0767

84

-0.07576 0.4934

84

-0.10847 0.3260

84

-0.34465 0.0395

36

TrendxD2010 -0.10826 0.3270

84

1.00000

84

-0.29443 0.0066

84

0.71688 <.0001

84

-0.04990 0.6521

84

0.54332 0.0006

36

TrendxQ12017A 0.19419 0.0767

84

-0.29443 0.0066

84

1.00000

84

-0.20604 0.0601

84

-0.29500 0.0064

84

-0.64176 <.0001

36

DQ2Q42010 -0.07576 0.4934

84

0.71688 <.0001

84

-0.20604 0.0601

84

1.00000

84

-0.03492 0.7525

84

0.37000 0.0263

36

D2011 -0.10847 0.3260

84

-0.04990 0.6521

84

-0.29500 0.0064

84

-0.03492 0.7525

84

1.00000

84

0.43855 0.0075

36

CUST -0.34465 0.0395

36

0.54332 0.0006

36

-0.64176 <.0001

36

0.37000 0.0263

36

0.43855 0.0075

36

1.00000

36



Page A-99

BROCKTON RESIDENTIAL NON-HEAT LOW CUSTOMER COUNT FULL MODEL




Ordinary Least Squares Estimates

SSE 7290.44851 DFE 23

MSE 316.97602 Root MSE 17.80382

SBC 339.938171 AIC 319.352425

MAE 12.0389638 AICC 335.89788

MAPE 0.13113716 HQC 326.537402


NOTE: Pr<DW is the p-value for testing positive autocorrelation, and Pr>DW is the p-value for testing negative autocorrelation.



Chow 20 13 10 0.15 0.9989

Tests for ARCH Disturbances Based on OLS Residuals


1 0.8677 0.3516 0.3106 0.5773

2 1.2263 0.5416 0.3574 0.8364

3 1.9094 0.5914 0.5990 0.8967

4 5.7384 0.2196 1.5250 0.8222

5 11.0800 0.0498 3.3885 0.6403

6 11.4652 0.0750 3.9692 0.6809

7 12.1939 0.0944 4.7130 0.6949

8 15.2058 0.0553 6.1120 0.6347

9 18.4318 0.0305 7.1577 0.6207

10 19.0860 0.0392 7.6680 0.6612

11 23.0943 0.0171 8.7040 0.6492

12 30.4105 0.0024 10.3013 0.5895



Page A-100




Parameter Estimates



Intercept 1 13696 49.4990 276.69 <.0001

TREND 1 -130.9649 1.5586 -84.03 <.0001

Q12015after 1 -2672 110.5810 -24.16 <.0001

TrendxQ12015after 1 66.2185 2.8425 23.30 <.0001

Q1 1 -200.9641 11.3274 -17.74 <.0001

Q2 1 -129.4158 11.3609 -11.39 <.0001

Q4 1 47.7950 8.7667 5.45 <.0001

Q1xQ15A 1 170.6633 16.2595 10.50 <.0001

Q2xQ15A 1 107.7908 15.4094 7.00 <.0001

TrendxD2010 1 -20.1494 1.3045 -15.45 <.0001

TrendxQ12017A 1 1.4661 0.4415 3.32 0.0030

DQ2Q42010 1 53.8920 19.9437 2.70 0.0127

D2011 1 -142.8188 16.1471 -8.84 <.0001



4 0.041878 0.17 0.8672

7 0.062428 0.26 0.7991

5 0.089518 0.39 0.6995

1 -0.107393 -0.49 0.6310

2 0.141341 0.64 0.5298

8 0.177054 0.85 0.4040

3 0.192657 0.93 0.3623

6 0.290205 1.42 0.1689



Page A-101






Page A-102

FULL MODEL FORECAST


Obs pred res Year Q CUST PCTDIFF SRES

1 10925.85 -12.1828 2010 1 10913.67 -.001116288 -0.68428

2 10900.18 -20.5089 2010 2 10879.67 -.001885062 -1.15194

3 10824.59 8.7416 2010 3 10833.33 0.000806914 0.49099

4 10775.16 20.5089 2010 4 10795.67 0.001899730 1.15194

5 10601.71 -6.0444 2011 1 10595.67 -.000570457 -0.33950

6 10542.30 16.3721 2011 2 10558.67 0.001550587 0.91959

7 10540.75 -11.4188 2011 3 10529.33 -.001084476 -0.64137

8 10457.58 1.0910 2011 4 10458.67 0.000104320 0.06128

9 10220.67 -25.6737 2012 1 10195 -.002518265 -1.44203

10 10161.26 -6.2572 2012 2 10155 -.000616169 -0.35145

11 10159.71 9.6219 2012 3 10169.33 0.000946165 0.54044

12 10076.54 8.4617 2012 4 10085 0.000839040 0.47528

13 9696.81 25.8557 2013 1 9722.67 0.002659324 1.45226

14 9637.40 1.6022 2013 2 9639 0.000166225 0.08999

15 9635.85 14.1513 2013 3 9650 0.001466455 0.79485

16 9552.68 0.9912 2013 4 9553.67 0.000103745 0.05567

17 9172.95 18.0452 2014 1 9191 0.001963351 1.01356

18 9113.54 8.7917 2014 2 9122.33 0.000963753 0.49381

19 9111.99 -25.6593 2014 3 9086.33 -.002823942 -1.44122

20 9028.82 -26.4894 2014 4 9002.33 -.002942507 -1.48785

21 8598.11 17.5600 2015 1 8615.67 0.002038148 0.98631

22 8542.04 -8.7094 2015 2 8533.33 -.001020638 -0.48919

23 8498.92 -19.9181 2015 3 8479 -.002349104 -1.11875

24 8481.97 -9.6367 2015 4 8472.33 -.001137430 -0.54127

25 8339.12 -2.1245 2016 1 8337 -.000254826 -0.11933

26 8283.05 10.2761 2016 2 8293.33 0.001239076 0.57718

27 8239.93 3.7374 2016 3 8243.67 0.000453372 0.20992

28 8222.98 7.6888 2016 4 8230.67 0.000934167 0.43186

29 8146.11 2.8867 2017 1 8149 0.000354237 0.16214

30 8091.51 16.8211 2017 2 8108.33 0.002074549 0.94480

31 8049.85 7.4764 2017 3 8057.33 0.000927903 0.41993

32 8034.37 -5.3683 2017 4 8029 -.000668614 -0.30153

33 7892.99 -18.3222 2018 1 7874.67 -.002326727 -1.02912

34 7838.39 -18.3878 2018 2 7820 -.002351375 -1.03280



Page A-103

FULL MODEL FORECAST


Obs pred res Year Q CUST PCTDIFF SRES

35 7796.73 13.2675 2018 3 7810 0.001698788 0.74521

36 7781.25 2.7528 2018 4 7784 0.000353650 0.15462



Page A-104

BROCKTON RESIDENTIAL NON-HEAT LOW CUSTOMER COUNT REGRESSION EX POST






Page A-105



Obs Year Q CUST xpred EXDIFF EXPCTDIFF

1 2018 1 7874.67 7911.89 -37.2199 -.004726532

2 2018 2 7820 7857.18 -37.1803 -.004754513

3 2018 3 7810 7805.19 4.8099 0.000615858

4 2018 4 7784 7792.40 -8.3950 -.001078496



Page A-106


03:19 Monday, October 28, 2019 10



2 D2011 Parameter Estimate for D2011 -142.82 -142.88 (.05%) 0.06

3 DQ2Q42010 Parameter Estimate for DQ2Q42010 53.89 53.63 .48% 0.26

4 Intercept Intercept Parameter 13695.76 13695.78 (.00%) -0.02

5 Q1 Parameter Estimate for Q1 -200.96 -200.77 .10% -0.19

6 Q12015after Parameter Estimate for Q12015after -2671.73 -2776.40 (3.8%) 104.67

7 Q1xQ15A Parameter Estimate for Q1xQ15A 170.66 185.35 (7.9%) -14.68


9 Q2xQ15A Parameter Estimate for Q2xQ15A 107.79 120.09 ( 10%) -12.30


11 TREND Parameter Estimate for TREND -130.96 -130.97 (.01%) 0.01

12 TrendxD2010 Parameter Estimate for TrendxD2010 -20.15 -20.15 .01% -0.00

13 TrendxQ12015after Parameter Estimate for TrendxQ12015after 66.22 68.63 (3.5%) -2.42

14 TrendxQ12017A Parameter Estimate for TrendxQ12017A 1.47 1.28 15% 0.19









23 _MSE_ Estimate of Variance 316.98 309.69 2.4% 7.28

24 _SSE_ Sum of Squares Error 7290.45 5884.13 24% 1406.32



Page A-107

The CORR Procedure




UPC Q4xBMEDD Q2xBMEDD BMEDD ARNHP

UPC 1.00000

36

-0.03015 0.8615

36

-0.07122 0.6798

36

0.98119 <.0001

36

-0.07626 0.6584

36

Q4xBMEDD -0.03015 0.8615

36

1.00000

84

-0.33106 0.0021

84

0.06294 0.5695

84

0.01798 0.8710

84

Q2xBMEDD -0.07122 0.6798

36

-0.33106 0.0021

84

1.00000

84

-0.16230 0.1402

84

0.00589 0.9576

84

BMEDD 0.98119 <.0001

36

0.06294 0.5695

84

-0.16230 0.1402

84

1.00000

84

0.02214 0.8416

84

ARNHP -0.07626 0.6584

36

0.01798 0.8710

84

0.00589 0.9576

84

0.02214 0.8416

84

1.00000

84



Page A-108

Brockton Residential Non-Heating UPC Full Model



Dependent Variable UPC



Chow 20 5 26 1.47 0.2348



3 0.047568 0.23 0.8170

2 0.223640 1.22 0.2342

7 0.204185 1.12 0.2724

6 -0.160826 -0.92 0.3636

5 0.202651 1.16 0.2554

1 -0.184347 -1.09 0.2843

4 -0.285062 -1.68 0.1036



Error t Value

8 0.349317 0.171073 2.04



Page A-109




Yule-Walker Estimates

SSE 0.11044376 DFE 30

MSE 0.00368 Root MSE 0.06068

SBC -83.617879 AIC -93.118993

MAE 0.04586493 AICC -90.222441

MAPE 3.18382364 HQC -89.802849

Transformed Regression R-Square 0.9878




1 0.8033 0.3701 0.2854 0.5932

2 3.1964 0.2023 1.4677 0.4801

3 3.2101 0.3603 1.4974 0.6829

4 5.4036 0.2483 2.5212 0.6408

5 5.6396 0.3429 2.7159 0.7437

6 7.4443 0.2817 2.8280 0.8301

7 8.3692 0.3012 3.2310 0.8628

8 12.0990 0.1468 4.2285 0.8359

9 12.2983 0.1970 4.2287 0.8957

10 12.4661 0.2551 4.2287 0.9364

11 18.3665 0.0735 7.3419 0.7708

12 22.8326 0.0292 7.5288 0.8208



Page A-110




Parameter Estimates



Intercept 1 1.7269 0.2086 8.28 <.0001

Q4xBMEDD 1 -0.000022 0.0000119 -1.83 0.0778

Q2xBMEDD 1 0.0000423 0.0000155 2.73 0.0106

BMEDD 1 0.000327 6.7023E-6 48.79 <.0001

ARNHP 1 -0.0386 0.0106 -3.66 0.0010



Page A-111






Page A-112

FULL MODEL FORECAST


Obs pred res Year Q UPC DIFF PCTDIFF SRES

1 2.06799 -0.06574 2010 1 1.99783116 -0.07016 -0.035117 -1.08345

2 1.32732 0.05740 2010 2 1.388583477 0.06126 0.044117 0.94605

3 0.96607 0.02502 2010 3 0.992769536 0.02670 0.026896 0.41236

4 1.46815 -0.01120 2010 4 1.456201421 -0.01195 -0.008205 -0.18451

5 2.12156 -0.10919 2011 1 2.005033188 -0.11653 -0.058119 -1.79958

6 1.42960 0.04168 2011 2 1.474080542 0.04448 0.030173 0.68687

7 0.97745 0.03252 2011 3 1.012156519 0.03471 0.034292 0.53600

8 1.40904 0.05429 2011 4 1.466980983 0.05794 0.039494 0.89472

9 1.97094 0.00480 2012 1 1.975740069 0.00480 0.002431 0.07915

10 1.36471 0.00762 2012 2 1.372328902 0.00762 0.005554 0.12563

11 1.02907 -0.07184 2012 3 0.957224321 -0.07184 -0.075052 -1.18404

12 1.54158 0.07448 2012 4 1.616063461 0.07448 0.046088 1.22754

13 2.17039 -0.02376 2013 1 2.146632561 -0.02376 -0.011066 -0.39152

14 1.51135 -0.06404 2013 2 1.447315074 -0.06404 -0.044246 -1.05542

15 1.03858 -0.04300 2013 3 0.995578238 -0.04300 -0.043191 -0.70870

16 1.55510 0.06965 2013 4 1.624750489 0.06965 0.042871 1.14799

17 2.21116 0.13758 2014 1 2.3487466 0.13758 0.058577 2.26753

18 1.46629 0.08471 2014 2 1.550993003 0.08471 0.054614 1.39607

19 0.99314 0.03456 2014 3 1.027697651 0.03456 0.033630 0.56961

20 1.39899 0.04323 2014 4 1.442218848 0.04323 0.029976 0.71251

21 2.19286 -0.00993 2015 1 2.182921351 -0.00993 -0.004551 -0.16372

22 1.43530 -0.03468 2015 2 1.400625547 -0.03468 -0.024758 -0.57151

23 0.95204 0.06781 2015 3 1.019852577 0.06781 0.066493 1.11764

24 1.35888 -0.05019 2015 4 1.308691942 -0.05019 -0.038352 -0.82722

25 1.91759 -0.05913 2016 1 1.858462277 -0.05913 -0.031818 -0.97457

26 1.43201 -0.04435 2016 2 1.387660928 -0.04435 -0.031963 -0.73100

27 0.97618 -0.02988 2016 3 0.946301829 -0.02988 -0.031577 -0.49248

28 1.47713 -0.10871 2016 4 1.368418367 -0.10871 -0.079445 -1.79173

29 2.00265 0.01441 2017 1 2.017057308 0.01441 0.007143 0.23747

30 1.47355 -0.01118 2017 2 1.462364013 -0.01118 -0.007649 -0.18434

31 0.96833 0.01541 2017 3 0.983741512 0.01541 0.015666 0.25400

32 1.45356 -0.04351 2017 4 1.410054801 -0.04351 -0.030855 -0.71706

33 2.07199 0.05567 2018 1 2.127666302 0.05567 0.026166 0.91755

34 1.41409 0.00850 2018 2 1.422592072 0.00850 0.005976 0.14012



Page A-113

FULL MODEL FORECAST



35 0.91780 -0.02126 2018 3 0.896542894 -0.02126 -0.023709 -0.35032

36 1.48778 -0.02019 2018 4 1.467583505 -0.02019 -0.013760 -0.33281



Page A-114

Brockton Residential Non-Heating UPC Ex Post Model






Page A-115



Obs Year Q UPC xpred EXDIFF EXPCTDIFF

1 2018 1 2.127666302 2.03071 0.096954 0.045568

2 2018 2 1.422592072 1.40544 0.017156 0.012060

3 2018 3 0.896542894 0.91817 -0.021628 -0.024124

4 2018 4 1.467583505 1.46596 0.001628 0.001110



Page A-116


03:21 Monday, October 28, 2019 10


1 ARNHP Parameter Estimate for ARNHP -0.03859 -0.03015 28% -0.00844

2 BMEDD Parameter Estimate for BMEDD 0.00033 0.00032 1.2% 0.00000

3 Intercept Intercept Parameter 1.72686 1.56183 11% 0.16503

4 Q2xBMEDD Parameter Estimate for Q2xBMEDD 0.00004 0.00005 (8.9%) -0.00000

5 Q4xBMEDD Parameter Estimate for Q4xBMEDD -0.00002 -0.00002 18% -0.00000

6 UPC Parameter Estimate for UPC -1.00000 -1.00000 .00% 0.00000













Page A-117

MULTICOLLINEARITY TEST

The CORR Procedure




cust Q1 Q4 enm ENMD1 ENMD2 ENMD3

cust BROCKTON COMMERCIAL & INDUSTRIAL CUSTOMER COUNT

1.00000

40

0.29397 0.0656

40

0.15963 0.3252

40

0.83264 <.0001

40

-0.47477 0.0020

40

-0.25551 0.1115

40

0.19767 0.2215

40

Q1 QUARTER 1 INTERCEPT SHIFT

0.29397 0.0656

40

1.00000

88

-0.33333 0.0015

88

-0.02628 0.8080

88

-0.00035 0.9974

88

-0.02897 0.7887

88

-0.00018 0.9987

88


0.15963 0.3252

40

-0.33333 0.0015

88

1.00000

88

0.02819 0.7943

88

-0.00091 0.9933

88

0.08501 0.4310

88

-0.00056 0.9959

88

enm NON MFG EMPLOYMENT

0.83264 <.0001

40

-0.02628 0.8080

88

0.02819 0.7943

88

1.00000

88

-0.70634 <.0001

88

-0.28607 0.0069

88

0.01874 0.8624

88

ENMD1 INTERACTION TERM FOR NON MFG EMPLOYMENT FROM 2009Q4 AND 2012Q3

-0.47477 0.0020

40

-0.00035 0.9974

88

-0.00091 0.9933

88

-0.70634 <.0001

88

1.00000

88

-0.09752 0.3661

88

-0.08671 0.4218

88


-0.25551 0.1115

40

-0.02897 0.7887

88

0.08501 0.4310

88

-0.28607 0.0069

88

-0.09752 0.3661

88

1.00000

88

-0.05356 0.6202

88


0.19767 0.2215

40

-0.00018 0.9987

88

-0.00056 0.9959

88

0.01874 0.8624

88

-0.08671 0.4218

88

-0.05356 0.6202

88

1.00000

88



Page A-118

BROCKTON COMMERCIAL & INDUSTRIAL LOW CUSTOMER COUNT REGRESSION



Dependent Variable cust

BROCKTON COMMERCIAL & INDUSTRIAL CUSTOMER COUNT



Chow 24 7 26 0.51 0.8182



1 -0.022624 -0.14 0.8869

2 0.052039 0.35 0.7254

3 0.239984 1.66 0.1066

Preliminary MSE 10158.8



Error t Value

4 -0.553231 0.147260 -3.76

Expected Autocorrelations

Lag Autocorr

0 1.0000

1 0.0000

2 0.0000

3 0.0000

4 0.5532



Page A-119





SSE 359695.303 DFE 32

MSE 11240 Root MSE 106.02112

SBC 509.086397 AIC 495.575362

MAE 77.6457903 AICC 500.220523

MAPE 0.55810829 HQC 500.460525



Observations 40



1 0.2937 0.5879 0.1005 0.7513

2 1.5657 0.4571 0.6923 0.7074

3 1.6150 0.6560 0.7587 0.8593

4 3.3914 0.4946 2.2904 0.6825

5 3.4102 0.6370 2.5286 0.7722

6 3.8291 0.6998 3.4613 0.7491

7 4.0293 0.7764 4.2319 0.7527

8 4.2110 0.8376 4.2469 0.8342

9 5.3826 0.7998 4.8010 0.8513

10 16.5090 0.0860 12.3936 0.2596

11 16.9577 0.1091 12.4016 0.3342

12 17.9091 0.1185 12.4325 0.4116



Page A-120




Parameter Estimates


Error t Value Approx Pr > |t| Variable Label

Intercept 1 3949 746.9831 5.29 <.0001

Q1 1 433.5921 92.9900 4.66 <.0001 QUARTER 1 INTERCEPT SHIFT

Q4 1 288.8613 93.9807 3.07 0.0043 QUARTER 4 INTERCEPT SHIFT

enm 1 13.8209 1.0537 13.12 <.0001 NON MFG EMPLOYMENT

ENMD1 1 0.2850 0.0854 3.34 0.0021 INTERACTION TERM FOR NON MFG EMPLOYMENT FROM 2009Q4 AND 2012Q3

ENMD2 1 -0.2423 0.0775 -3.13 0.0038 INTERACTION TERM FOR NON MFG EMPLOYMENT FROM 2012Q4 AND 2013Q4

ENMD3 1 -0.3589 0.0634 -5.67 <.0001 INTERACTION TERM FOR NON MFG EMPLOYMENT FROM 2016Q4 AND 2017Q3

AR4 1 -0.6143 0.1482 -4.15 0.0002



Page A-121






Page A-122

FULL MODEL FORECAST


Obs PRED RES YEAR Q cust DIFF PCTDIFF SRES

1 13542.39 -2.935 2009 1 13538.66667 -3.720 -0.000275 -0.02769

2 13035.68 85.999 2009 2 13144.66667 108.984 0.008291 0.81115

3 13016.70 -84.198 2009 3 12910 -106.702 -0.008265 -0.79416

4 13500.75 -1.642 2009 4 13498.66667 -2.081 -0.000154 -0.01549

5 13651.42 88.911 2010 1 13740.33333 88.911 0.006471 0.83862

6 13295.24 69.091 2010 2 13364.33333 69.091 0.005170 0.65167

7 13181.51 -97.180 2010 3 13084.33333 -97.180 -0.007427 -0.91661

8 13558.57 -68.240 2010 4 13490.33333 -68.240 -0.005058 -0.64364

9 13781.77 -14.108 2011 1 13767.66667 -14.108 -0.001025 -0.13307

10 13402.51 16.486 2011 2 13419 16.486 0.001229 0.15550

11 13251.91 -51.241 2011 3 13200.66667 -51.241 -0.003882 -0.48331

12 13635.50 -21.497 2011 4 13614 -21.497 -0.001579 -0.20276

13 13884.38 1.954 2012 1 13886.33333 1.954 0.000141 0.01843

14 13525.60 61.729 2012 2 13587.33333 61.729 0.004543 0.58223

15 13420.12 -168.791 2012 3 13251.33333 -168.791 -0.012738 -1.59205

16 13457.32 -44.657 2012 4 13412.66667 -44.657 -0.003329 -0.42121

17 13709.86 -51.860 2013 1 13658 -51.860 -0.003797 -0.48915

18 13388.44 -18.441 2013 2 13370 -18.441 -0.001379 -0.17394

19 13204.00 53.336 2013 3 13257.33333 53.336 0.004023 0.50307

20 13653.67 217.662 2013 4 13871.33333 217.662 0.015692 2.05301

21 14049.58 70.755 2014 1 14120.33333 70.755 0.005011 0.66737

22 13725.85 117.481 2014 2 13843.33333 117.481 0.008486 1.10809

23 13669.64 -80.978 2014 3 13588.66667 -80.978 -0.005959 -0.76379

24 14167.64 -61.637 2014 4 14106 -61.637 -0.004370 -0.58136

25 14281.40 68.596 2015 1 14350 68.596 0.004780 0.64701

26 13952.80 128.538 2015 2 14081.33333 128.538 0.009128 1.21238

27 13816.73 49.272 2015 3 13866 49.272 0.003553 0.46474

28 14272.19 38.815 2015 4 14311 38.815 0.002712 0.36610

29 14504.07 75.602 2016 1 14579.66667 75.602 0.005185 0.71308

30 14197.92 173.742 2016 2 14371.66667 173.742 0.012089 1.63875

31 14074.11 -67.774 2016 3 14006.33333 -67.774 -0.004839 -0.63925

32 14193.98 -111.982 2016 4 14082 -111.982 -0.007952 -1.05622

33 14413.60 -119.262 2017 1 14294.33333 -119.262 -0.008343 -1.12489

34 14117.38 -50.709 2017 2 14066.66667 -50.709 -0.003605 -0.47829

35 13895.65 14.347 2017 3 13910 14.347 0.001031 0.13533



Page A-123

FULL MODEL FORECAST



36 14488.15 -167.155 2017 4 14321 -167.155 -0.011672 -1.57662

37 14680.24 -114.574 2018 1 14565.66667 -114.574 -0.007866 -1.08068

38 14379.08 -54.750 2018 2 14324.33333 -54.750 -0.003822 -0.51641

39 14299.18 -97.849 2018 3 14201.33333 -97.849 -0.006890 -0.92292

40 14520.28 222.056 2018 4 14742.33333 222.056 0.015062 2.09445



Page A-124

BROCKTON COMMERCIAL & INDUSTRIAL LOW CUSTOMER COUNT REGRESSION EX POST




BROCKTON COMMERCIAL & INDUSTRIAL CUSTOMER COUNT



Page A-125



Obs YEAR Q cust XPPRED EXDIFF EXPCTDIFF

1 2018 1 14565.66667 14737.28 -171.612 -0.011782

2 2018 2 14324.33333 14452.19 -127.860 -0.008926

3 2018 3 14201.33333 14355.71 -154.376 -0.010871

4 2018 4 14742.33333 14496.48 245.853 0.016677



Page A-126


03:22 Monday, October 28, 2019 10


1 ENMD1 Parameter Estimate for ENMD1 0.29 0.31 (7.5%) -0.02

2 ENMD2 Parameter Estimate for ENMD2 -0.24 -0.23 5.1% -0.01

3 ENMD3 Parameter Estimate for ENMD3 -0.36 -0.43 ( 17%) 0.08



6 Q4 Parameter Estimate for Q4 288.86 202.27 43% 86.59





11 _LIKLHD_ Log-Likelihood -239.79 -212.18 13% -27.61

12 _MSE_ Estimate of Variance 11240.48 9168.34 23% 2072.13


14 cust Parameter Estimate for cust -1.00 -1.00 .00% 0.00

15 enm Parameter Estimate for enm 13.82 14.42 (4.1%) -0.59



Page A-127


The CORR Procedure




UPC BMEDD ACILLP DQ118AxBMEDD DQ1Q215 Q4 Q2

UPC 1.00000

52

0.98618 <.0001

52

0.01227 0.9312

52

0.20974 0.1356

52

0.21856 0.1196

52

-0.12147 0.3910

52

-0.14939 0.2905

52

BMEDD 0.98618 <.0001

52

1.00000

100

0.02118 0.8343

100

0.50920 <.0001

100

0.13340 0.1858

100

0.05327 0.5986

100

-0.16461 0.1017

100

ACILLP 0.01227 0.9312

52

0.02118 0.8343

100

1.00000

100

-0.35106 0.0003

100

0.05020 0.6199

100

-0.02164 0.8307

100

0.00463 0.9635

100

DQ118AxBMEDD 0.20974 0.1356

52

0.50920 <.0001

100

-0.35106 0.0003

100

1.00000

100

-0.10285 0.3085

100

0.04506 0.6562

100

-0.08983 0.3741

100

DQ1Q215 0.21856 0.1196

52

0.13340 0.1858

100

0.05020 0.6199

100

-0.10285 0.3085

100

1.00000

100

-0.08248 0.4146

100

0.08248 0.4146

100

Q4 -0.12147 0.3910

52

0.05327 0.5986

100

-0.02164 0.8307

100

0.04506 0.6562

100

-0.08248 0.4146

100

1.00000

100

-0.33333 0.0007

100

Q2 -0.14939 0.2905

52

-0.16461 0.1017

100

0.00463 0.9635

100

-0.08983 0.3741

100

0.08248 0.4146

100

-0.33333 0.0007

100

1.00000

100



Page A-128

Brockton C&I LLF UPC Full Model





SSE 69.7057726 DFE 45

MSE 1.54902 Root MSE 1.24460

SBC 190.466363 AIC 176.807657

MAE 0.94563635 AICC 179.353112

MAPE 4.4364642 HQC 182.044083




Chow 20 7 38 0.41 0.8926



1 1.9354 0.1642 0.8574 0.3545

2 2.9122 0.2331 1.5219 0.4672

3 7.2646 0.0639 3.5113 0.3193

4 7.3920 0.1166 3.5395 0.4719

5 7.4620 0.1885 3.5723 0.6125

6 7.7561 0.2565 3.8537 0.6965

7 14.0421 0.0504 7.6762 0.3620

8 15.5102 0.0500 8.2972 0.4050

9 15.5177 0.0777 8.3901 0.4954

10 19.2158 0.0376 8.3904 0.5908

11 20.5795 0.0380 8.6921 0.6503

12 21.3449 0.0456 8.7749 0.7220



Page A-129




Parameter Estimates



Intercept 1 7.2131 0.8674 8.32 <.0001

BMEDD 1 0.0252 0.000152 166.61 <.0001

ACILLP 1 -0.1122 0.0576 -1.95 0.0576

DQ118AxBMEDD 1 0.002292 0.000323 7.10 <.0001

DQ1Q215 1 5.1616 0.9296 5.55 <.0001

Q4 1 -11.6327 0.4245 -27.41 <.0001

Q2 1 -3.9390 0.4310 -9.14 <.0001



Page A-130






Page A-131

FULL MODEL FORECAST


Obs res Year Q UPC DIFF PCTDIFF SRES

1 0.67370 2006 1 87.21815514 0.67370 0.00772 0.54130

2 -1.45579 2006 2 31.97607605 -1.45579 -0.04553 -1.16969

3 0.68708 2006 3 7.785377424 0.68708 0.08825 0.55205

4 0.31720 2006 4 30.45851188 0.31720 0.01041 0.25486

5 -1.74207 2007 1 89.47151827 -1.74207 -0.01947 -1.39971

6 0.41400 2007 2 35.4060238 0.41400 0.01169 0.33264

7 -0.67537 2007 3 6.908718843 -0.67537 -0.09776 -0.54264

8 0.26060 2007 4 36.2831049 0.26060 0.00718 0.20939

9 1.35664 2008 1 89.05488435 1.35664 0.01523 1.09003

10 0.22266 2008 2 34.04469045 0.22266 0.00654 0.17891

11 1.11212 2008 3 7.989234924 1.11212 0.13920 0.89356

12 -0.48613 2008 4 37.88850909 -0.48613 -0.01283 -0.39059

13 -1.93938 2009 1 94.67520187 -1.93938 -0.02048 -1.55824

14 1.63264 2009 2 34.24806004 1.63264 0.04767 1.31178

15 1.12794 2009 3 9.638884586 1.12794 0.11702 0.90627

16 -1.32838 2009 4 34.3371197 -1.32838 -0.03869 -1.06732

17 2.63886 2010 1 93.36590577 2.63886 0.02826 2.12025

18 0.22540 2010 2 26.32853616 0.22540 0.00856 0.18110

19 0.07255 2010 3 7.113953075 0.07255 0.01020 0.05829

20 -1.23671 2010 4 37.17217268 -1.23671 -0.03327 -0.99366

21 1.29360 2011 1 97.54199453 1.29360 0.01326 1.03937

22 0.14772 2011 2 35.02757284 0.14772 0.00422 0.11869

23 -0.54674 2011 3 6.839604058 -0.54674 -0.07994 -0.43929

24 0.77267 2011 4 31.20865776 0.77267 0.02476 0.62082

25 -1.08549 2012 1 78.27921937 -1.08549 -0.01387 -0.87216

26 -1.47408 2012 2 25.98766008 -1.47408 -0.05672 -1.18439

27 -0.21302 2012 3 7.068772956 -0.21302 -0.03014 -0.17115

28 0.40099 2012 4 36.13991748 0.40099 0.01110 0.32218

29 -0.27626 2013 1 89.61053353 -0.27626 -0.00308 -0.22197

30 -2.25100 2013 2 33.59491399 -2.25100 -0.06700 -1.80862

31 -1.17132 2013 3 6.783993766 -1.17132 -0.17266 -0.94113

32 -0.92949 2013 4 39.41911377 -0.92949 -0.02358 -0.74682

33 0.64468 2014 1 101.5946272 0.64468 0.00635 0.51798

34 -0.80433 2014 2 36.01358055 -0.80433 -0.02233 -0.64626

35 -0.62940 2014 3 6.923392041 -0.62940 -0.09091 -0.50571



Page A-132

FULL MODEL FORECAST


Obs res Year Q UPC DIFF PCTDIFF SRES

36 0.24041 2014 4 36.99012241 0.24041 0.00650 0.19316

37 0.63278 2015 1 113.0972125 0.63278 0.00560 0.50842

38 -0.63278 2015 2 40.14719251 -0.63278 -0.01576 -0.50842

39 1.65828 2015 3 8.584186737 1.65828 0.19318 1.33238

40 1.06400 2015 4 31.72738919 1.06400 0.03354 0.85490

41 0.19525 2016 1 82.13621711 0.19525 0.00238 0.15688

42 2.41486 2016 2 36.65561868 2.41486 0.06588 1.94027

43 0.51853 2016 3 7.644184775 0.51853 0.06783 0.41662

44 -0.72322 2016 4 35.38178762 -0.72322 -0.02044 -0.58109

45 -1.78091 2017 1 83.73360075 -1.78091 -0.02127 -1.43091

46 -0.25679 2017 2 36.31097155 -0.25679 -0.00707 -0.20633

47 -2.09726 2017 3 5.965995687 -2.09726 -0.35154 -1.68509

48 1.92506 2017 4 34.26797477 1.92506 0.05618 1.54674

49 -0.57362 2018 1 98.01839939 -0.57362 -0.00585 -0.46089

50 1.81750 2018 2 40.59479209 1.81750 0.04477 1.46031

51 0.11884 2018 3 6.949324009 0.11884 0.01710 0.09548

52 -0.27700 2018 4 44.73321728 -0.27700 -0.00619 -0.22256



Page A-133

Brockton C&I LLF UPC Ex Post Model






Page A-134



Year Q UPC xpred EXDIFF EXPCTDIFF

1 2018 1 98.01839939 90.9136 7.10484 0.07248

2 2018 2 40.59479209 35.5467 5.04806 0.12435

3 2018 3 6.949324009 6.7347 0.21460 0.03088

4 2018 4 44.73321728 40.8022 3.93105 0.08788



Page A-135




1 ACILLP Parameter Estimate for ACILLP -0.1122 -0.1107 1.3% -0.00145

2 BMEDD Parameter Estimate for BMEDD 0.0252 0.0253 (.05%) -0.00001

3 DQ118AxBMEDD Parameter Estimate for DQ118AxBMEDD 0.0023 0.0000 . 0.00229

4 DQ1Q215 Parameter Estimate for DQ1Q215 5.1616 5.2159 (1.0%) -0.05427

5 Intercept Intercept Parameter 7.2131 7.1860 .38% 0.02710

6 Q2 Parameter Estimate for Q2 -3.9390 -4.1052 (4.0%) 0.16622











17 _MSE_ Estimate of Variance 1.5490 1.5636 (.93%) -0.01455

18 _SSE_ Sum of Squares Error 69.7058 65.6698 6.1% 4.03599



Page A-136


The CORR Procedure



LagBDevfromNorm ACIHLP3 emf0 d5 d7 d8 d9 d12

LagBDevfromNorm Q4-Q3 EDD Deviation from Normal

1.00000

100

0.19749 0.0525

97

0.13127 0.1930

100

0.59214 <.0001

100

0.01669 0.8691

100

0.03039 0.7640

100

-0.09504 0.3469

100

-0.32318 0.0010

100

ACIHLP3 Lag 3 Adjusted C&I High-Load Factor Price

0.19749 0.0525

97

1.00000

97

0.86414 <.0001

97

0.13225 0.1966

97

0.13179 0.1982

97

0.13263 0.1953

97

-0.63738 <.0001

97

0.04657 0.6506

97

emf0 Manufacturing Employment

0.13127 0.1930

100

0.86414 <.0001

97

1.00000

100

0.00851 0.9330

100

0.02025 0.8415

100

0.01986 0.8445

100

-0.63715 <.0001

100

0.02319 0.8188

100

d5 Dummy for data Q3 2014 to Q3 2016

0.59214 <.0001

100

0.13225 0.1966

97

0.00851 0.9330

100

1.00000

100

-0.07945 0.4320

100

-0.07933 0.4327

100

-0.30826 0.0018

100

-0.06388 0.5278

100

d7 Dummy for data between Q1 2011 to Q2 2012

0.01669 0.8691

100

0.13179 0.1982

97

0.02025 0.8415

100

-0.07945 0.4320

100

1.00000

100

0.99844 <.0001

100

-0.24764 0.0130

100

-0.05132 0.6121

100

d8 Interaction term for Q1 2011 to Q2 2012 * Lag EDD deviation

0.03039 0.7640

100

0.13263 0.1953

97

0.01986 0.8445

100

-0.07933 0.4327

100

0.99844 <.0001

100

1.00000

100

-0.24725 0.0131

100

-0.05124 0.6127

100

d9 Dummy for Q4 2018 reclassification

-0.09504 0.3469

100

-0.63738 <.0001

97

-0.63715 <.0001

100

-0.30826 0.0018

100

-0.24764 0.0130

100

-0.24725 0.0131

100

1.00000

100

-0.19909 0.0471

100

d12 Interaction term for Q3 2012 to Q2 2013 * Lag EDD Deviation

-0.32318 0.0010

100

0.04657 0.6506

97

0.02319 0.8188

100

-0.06388 0.5278

100

-0.05132 0.6121

100

-0.05124 0.6127

100

-0.19909 0.0471

100

1.00000

100



Page A-137

Brockton C & I HLF Customer Count Full Model




SSE 68934.8908 DFE 40

MSE 1723 Root MSE 41.51352

SBC 529.288133 AIC 512.26175

MAE 31.886105 AICC 516.877135

MAPE 1.12625108 HQC 518.721536




Chow 24 9 31 1.11 0.3822



1 0.1270 0.7216 0.1263 0.7223

2 3.2005 0.2018 2.9097 0.2334

3 8.0192 0.0456 6.1921 0.1026

4 8.5195 0.0743 6.8405 0.1446

5 8.6494 0.1239 6.9795 0.2222

6 12.9294 0.0442 9.9651 0.1261

7 13.1387 0.0688 9.9803 0.1897

8 19.5715 0.0121 17.7211 0.0234

9 20.2255 0.0166 18.1542 0.0334

10 21.4294 0.0183 18.5811 0.0459

11 25.4998 0.0077 22.0029 0.0244

12 26.5154 0.0091 23.1474 0.0265



Page A-138



Parameter Estimates



Intercept 1 3483 212.9427 16.36 <.0001

LagBDevfromNorm 1 -1187 142.5868 -8.33 <.0001 Q4-Q3 EDD Deviation from Normal

ACIHLP3 1 -17.4951 3.6242 -4.83 <.0001 Lag 3 Adjusted C&I High-Load Factor Price

emf0 1 12.8923 2.6023 4.95 <.0001 Manufacturing Employment

d5 1 -80.2823 23.5436 -3.41 0.0015 Dummy for data Q3 2014 to Q3 2016

d7 1 -730.8957 339.5724 -2.15 0.0375 Dummy for data between Q1 2011 to Q2 2012

d8 1 673.5903 337.0573 2.00 0.0525 Interaction term for Q1 2011 to Q2 2012 * Lag EDD deviation

d9 1 -219.1401 43.1077 -5.08 <.0001 Dummy for Q4 2018 reclassification

d12 1 93.2657 26.7158 3.49 0.0012 Interaction term for Q3 2012 to Q2 2013 * Lag EDD Deviation



8 0.002667 0.02 0.9881

5 -0.010779 -0.06 0.9543

3 -0.018705 -0.10 0.9179

4 -0.041352 -0.27 0.7917

7 -0.167756 -1.05 0.3008

6 0.124694 0.82 0.4152

2 0.235515 1.49 0.1435

1 -0.300071 -1.96 0.0566



Page A-139





Page A-140

FULL MODEL FORECAST


1 . . 2006 1 2878.666667 . . .

2 . . 2006 2 2843 . . .

3 . . 2006 3 2854.333333 . . .

4 2923.70 -12.0361 2006 4 2911.666667 -12.0361 -0.004134 -0.28993

5 2907.39 6.2753 2007 1 2913.666667 6.2753 0.002154 0.15116

6 2892.90 -11.5648 2007 2 2881.333333 -11.5648 -0.004014 -0.27858

7 2899.25 -18.2523 2007 3 2881 -18.2523 -0.006335 -0.43967

8 2901.03 36.3068 2007 4 2937.333333 36.3068 0.012360 0.87458

9 2899.85 42.8217 2008 1 2942.666667 42.8217 0.014552 1.03151

10 2896.49 3.8410 2008 2 2900.333333 3.8410 0.001324 0.09252

11 2884.82 -28.1574 2008 3 2856.666667 -28.1574 -0.009857 -0.67827

12 2868.27 0.3976 2008 4 2868.666667 0.3976 0.000139 0.00958

13 2833.23 41.0998 2009 1 2874.333333 41.0998 0.014299 0.99003

14 2790.41 17.2579 2009 2 2807.666667 17.2579 0.006147 0.41572

15 2772.82 -15.8246 2009 3 2757 -15.8246 -0.005740 -0.38119

16 2681.95 36.0502 2009 4 2718 36.0502 0.013263 0.86840

17 2699.93 36.7405 2010 1 2736.666667 36.7405 0.013425 0.88503

18 2729.06 -12.7287 2010 2 2716.333333 -12.7287 -0.004686 -0.30661

19 2748.40 -19.0666 2010 3 2729.333333 -19.0666 -0.006986 -0.45929

20 2895.53 -62.2010 2010 4 2833.333333 -62.2010 -0.021953 -1.49833

21 2815.13 29.2077 2011 1 2844.333333 29.2077 0.010269 0.70357

22 2807.48 3.1869 2011 2 2810.666667 3.1869 0.001134 0.07677

23 2799.06 -32.3946 2011 3 2766.666667 -32.3946 -0.011709 -0.78034

24 2743.00 7.6714 2011 4 2750.666667 7.6714 0.002789 0.18479

25 2736.75 14.9149 2012 1 2751.666667 14.9149 0.005420 0.35928

26 2739.25 -22.5863 2012 2 2716.666667 -22.5863 -0.008314 -0.54407

27 2859.28 -22.6142 2012 3 2836.666667 -22.6142 -0.007972 -0.54474

28 3096.74 40.9307 2012 4 3137.666667 40.9307 0.013045 0.98596

29 3109.33 18.0075 2013 1 3127.333333 18.0075 0.005758 0.43378

30 3117.58 -30.9130 2013 2 3086.666667 -30.9130 -0.010015 -0.74465

31 3044.37 -58.6984 2013 3 2985.666667 -58.6984 -0.019660 -1.41396

32 2864.01 -44.6763 2013 4 2819.333333 -44.6763 -0.015846 -1.07619

33 2857.50 -37.8336 2014 1 2819.666667 -37.8336 -0.013418 -0.91136

34 2850.84 -70.1764 2014 2 2780.666667 -70.1764 -0.025237 -1.69045

35 2763.31 -51.6451 2014 3 2711.666667 -51.6451 -0.019046 -1.24406



Page A-141

FULL MODEL FORECAST


36 2621.72 31.2776 2014 4 2653 31.2776 0.011790 0.75343

37 2604.70 49.3031 2015 1 2654 49.3031 0.018577 1.18764

38 2604.28 21.7216 2015 2 2626 21.7216 0.008272 0.52324

39 2600.05 -24.0464 2015 3 2576 -24.0464 -0.009335 -0.57924

40 2591.39 -32.0549 2015 4 2559.333333 -32.0549 -0.012525 -0.77216

41 2613.36 -19.0243 2016 1 2594.333333 -19.0243 -0.007333 -0.45827

42 2621.32 -47.9820 2016 2 2573.333333 -47.9820 -0.018646 -1.15582

43 2636.88 72.4504 2016 3 2709.333333 72.4504 0.026741 1.74522

44 3000.19 42.8075 2016 4 3043 42.8075 0.014068 1.03117

45 3009.50 56.4984 2017 1 3066 56.4984 0.018427 1.36096

46 3008.27 41.0621 2017 2 3049.333333 41.0621 0.013466 0.98913

47 3007.21 -21.5462 2017 3 2985.666667 -21.5462 -0.007217 -0.51902

48 2913.28 45.7162 2017 4 2959 45.7162 0.015450 1.10124

49 2912.63 63.0382 2018 1 2975.666667 63.0382 0.021185 1.51850

50 2914.04 22.6245 2018 2 2936.666667 22.6245 0.007704 0.54499

51 2912.52 -85.1863 2018 3 2827.333333 -85.1863 -0.030130 -2.05201

52 2671.00 0.0000 2018 4 2671 0.0000 0.000000 0.00000



Page A-142



Obs YEAR Q CUST EXPRED EXDIFF EXPCTDIFF

1 2018 1 2975.666667 2912.45 63.213 0.021243

2 2018 2 2936.666667 2913.87 22.802 0.007764

3 2018 3 2827.333333 2912.35 -85.013 -0.030068

4 2018 4 2671 2890.00 -219.005 -0.081994



Page A-143



1 ACIHLP3 Parameter Estimate for ACIHLP3 -17.50 -17.46 .23% -0.04



4 LagBDevfromNorm Parameter Estimate for LagBDevfromNorm -1187.19 -1188.08 (.07%) 0.89












16 d12 Parameter Estimate for d12 93.27 93.26 .00% 0.00

17 d5 Parameter Estimate for d5 -80.28 -80.14 .17% -0.14

18 d7 Parameter Estimate for d7 -730.90 -731.55 (.09%) 0.66

19 d8 Parameter Estimate for d8 673.59 674.28 (.10%) -0.69

20 d9 Parameter Estimate for d9 -219.14 . . .

21 emf0 Parameter Estimate for emf0 12.89 12.88 .11% 0.01



Page A-144


The CORR Procedure



CIHLFUPC BMEDD ACIHLP LagBDevfromNorm d5

CIHLFUPC

1.00000

52

0.92901 <.0001

52

-0.13780 0.3300

52

0.01527 0.9145

52

0.27349 0.0498

52

BMEDD

0.92901 <.0001

52

1.00000

100

0.02916 0.7733

100

-0.01375 0.8920

100

0.15244 0.1300

100

ACIHLP Adjusted C&I High-Load Factor Price

-0.13780 0.3300

52

0.02916 0.7733

100

1.00000

140

0.17342 0.0405

140

0.05897 0.4889

140

LagBDevfromNorm Q4-Q3 EDD Deviation from Normal

0.01527 0.9145

52

-0.01375 0.8920

100

0.17342 0.0405

140

1.00000

172

0.11605 0.1295

172

d5 Dummy for Q1 2010

0.27349 0.0498

52

0.15244 0.1300

100

0.05897 0.4889

140

0.11605 0.1295

172

1.00000

172



Page A-145

Brockton C&I HLF UPC Full Model


Dependent Variable CIHLFUPC



Chow 24 5 42 0.63 0.6767



4 0.007956 0.04 0.9661

5 0.025444 0.18 0.8570

8 0.101063 0.68 0.4997

7 -0.124792 -0.85 0.3985




Error t Value

1 -0.547965 0.132777 -4.13

2 0.339584 0.148751 2.28

3 -0.290286 0.127019 -2.29

6 0.407226 0.114677 3.55



Page A-146




Lag Autocorr

0 1.0000

1 0.4231

2 -0.0744

3 0.0752

4 0.2196

5 -0.0991

6 -0.5143


SSE 1181.98353 DFE 43

MSE 27.48799 Root MSE 5.24290

SBC 347.89571 AIC 330.334516

MAE 3.92423592 AICC 334.62023

MAPE 3.29828926 HQC 337.067063



Observations 52



1 0.0508 0.8216 0.0385 0.8445

2 0.2459 0.8843 0.1724 0.9174

3 3.0292 0.3871 0.7653 0.8577

4 3.0293 0.5529 0.8449 0.9323

5 10.5375 0.0614 2.1342 0.8303

6 10.5529 0.1032 2.2122 0.8992

7 10.8380 0.1458 2.9037 0.8938



Page A-147





8 11.0890 0.1967 3.0271 0.9326

9 11.6471 0.2339 3.8450 0.9213

10 13.0659 0.2200 4.9306 0.8958

11 13.1010 0.2868 5.2072 0.9207

12 16.0865 0.1873 8.3023 0.7611

Parameter Estimates



Intercept 1 79.8839 13.5431 5.90 <.0001

BMEDD 1 0.0178 0.001421 12.50 <.0001

ACIHLP 1 -1.5347 0.2979 -5.15 <.0001 Adjusted C&I High-Load Factor Price

LagBDevfromNorm 1 29.9290 12.4504 2.40 0.0206 Q4-Q3 EDD Deviation from Normal

d5 1 11.0932 4.3053 2.58 0.0135 Dummy for Q1 2010

AR1 1 -0.5557 0.1396 -3.98 0.0003

AR2 1 0.3425 0.1563 2.19 0.0339

AR3 1 -0.2767 0.1282 -2.16 0.0365

AR6 1 0.4189 0.1145 3.66 0.0007



Page A-148





Page A-149

FULL MODEL FORECAST

Obs PUPC RUPC YEAR Q CIHLFUPC DIFF PCTDIFF SRES

1 143.378 0.1697 2006 1 143.6191524 0.2410 0.001678 0.03236

2 108.218 1.8237 2006 2 110.5532888 2.3356 0.021126 0.34784

3 87.603 -1.9596 2006 3 85.22211842 -2.3812 -0.027941 -0.37376

4 107.086 -1.8154 2006 4 105.0019462 -2.0843 -0.019850 -0.34625

5 144.090 2.7912 2007 1 147.2940167 3.2042 0.021754 0.53238

6 112.695 4.1989 2007 2 117.3196437 4.6242 0.039415 0.80087

7 90.844 3.2442 2007 3 94.08851093 3.2442 0.034481 0.61879

8 117.542 -3.1490 2007 4 114.3932138 -3.1490 -0.027528 -0.60062

9 145.169 10.5183 2008 1 155.6870186 10.5183 0.067561 2.00620

10 119.819 -0.5393 2008 2 119.2793932 -0.5393 -0.004521 -0.10287

11 87.112 -3.3002 2008 3 83.81143524 -3.3002 -0.039376 -0.62946

12 111.199 3.3654 2008 4 114.5641413 3.3654 0.029376 0.64190

13 149.408 -8.3647 2009 1 141.0436043 -8.3647 -0.059306 -1.59544

14 105.419 -0.4638 2009 2 104.9548854 -0.4638 -0.004419 -0.08847

15 88.101 -5.7778 2009 3 82.32317737 -5.7778 -0.070184 -1.10202

16 113.034 -4.6250 2009 4 108.4091244 -4.6250 -0.042663 -0.88215

17 161.202 4.2685 2010 1 165.4707674 4.2685 0.025796 0.81416

18 115.798 3.6946 2010 2 119.4928212 3.6946 0.030919 0.70468

19 99.217 -4.7986 2010 3 94.41853933 -4.7986 -0.050822 -0.91525

20 120.589 -3.1622 2010 4 117.4267059 -3.1622 -0.026929 -0.60314

21 158.581 -0.2114 2011 1 158.3699754 -0.2114 -0.001335 -0.04032

22 123.369 2.8169 2011 2 126.1860769 2.8169 0.022323 0.53728

23 96.634 -3.4776 2011 3 93.15626506 -3.4776 -0.037331 -0.66329

24 114.825 -5.2407 2011 4 109.5845856 -5.2407 -0.047824 -0.99959

25 145.085 -3.5458 2012 1 141.5387038 -3.5458 -0.025052 -0.67631

26 117.170 -3.9131 2012 2 113.2568098 -3.9131 -0.034551 -0.74637

27 94.849 -5.4635 2012 3 89.3853114 -5.4635 -0.061123 -1.04208

28 109.392 -3.1817 2012 4 106.2106661 -3.1817 -0.029956 -0.60685

29 144.771 2.0458 2013 1 146.8170966 2.0458 0.013935 0.39021

30 119.576 -2.5896 2013 2 116.9861771 -2.5896 -0.022136 -0.49393

31 92.629 3.5743 2013 3 96.20330468 3.5743 0.037154 0.68174

32 127.958 1.3380 2013 4 129.2955782 1.3380 0.010348 0.25520

33 164.164 9.7145 2014 1 173.8784726 9.7145 0.055869 1.85288

34 130.658 4.7369 2014 2 135.3945097 4.7369 0.034986 0.90348

35 101.507 5.3152 2014 3 106.8224954 5.3152 0.049757 1.01379



Page A-150

FULL MODEL FORECAST

Obs PUPC RUPC YEAR Q CIHLFUPC DIFF PCTDIFF SRES

36 129.019 -3.8837 2014 4 125.1351929 -3.8837 -0.031036 -0.74075

37 165.357 -4.3065 2015 1 161.050113 -4.3065 -0.026740 -0.82141

38 120.555 2.1830 2015 2 122.7381315 2.1830 0.017786 0.41637

39 96.012 5.6633 2015 3 101.6750776 5.6633 0.055700 1.08018

40 116.998 3.2384 2015 4 120.2366502 3.2384 0.026934 0.61768

41 146.772 0.1802 2016 1 146.952589 0.1802 0.001227 0.03438

42 123.644 1.5302 2016 2 125.1742228 1.5302 0.012225 0.29186

43 107.672 -8.3159 2016 3 99.35605315 -8.3159 -0.083698 -1.58612

44 120.671 -7.6642 2016 4 113.0069011 -7.6642 -0.067821 -1.46182

45 145.508 13.2409 2017 1 158.7485323 13.2409 0.083408 2.52550

46 129.629 -1.3661 2017 2 128.2627897 -1.3661 -0.010651 -0.26056

47 100.074 1.7065 2017 3 101.7801719 1.7065 0.016766 0.32548

48 127.026 -5.1670 2017 4 121.8591867 -5.1670 -0.042401 -0.98553

49 157.791 -3.4878 2018 1 154.3034614 -3.4878 -0.022604 -0.66525

50 125.665 -1.0930 2018 2 124.5724177 -1.0930 -0.008774 -0.20848

51 96.330 3.1565 2018 3 99.48667767 3.1565 0.031728 0.60205

52 124.166 8.6820 2018 4 132.8476226 8.6820 0.065353 1.65595



Page A-151

Brockton C&I HLF UPC Ex Post Model





Page A-152


Obs YEAR Q CIHLFUPC EXDIFF EXPCTDIFF

1 2018 1 154.3034614 . .

2 2018 2 124.5724177 . .

3 2018 3 99.48667767 . .

4 2018 4 132.8476226 . .



Page A-153



1 ACIHLP Parameter Estimate for ACIHLP -1.53 -1.47 4.1% -0.060

2 BMEDD Parameter Estimate for BMEDD 0.02 0.02 .16% 0.000

3 CIHLFUPC Parameter Estimate for CIHLFUPC -1.00 -1.00 .00% 0.000

4 Intercept Intercept Parameter 79.88 74.59 7.1% 5.291

5 LagBDevfromNorm Parameter Estimate for LagBDevfromNorm 29.93 34.34 ( 13%) -4.414

6 _A_1 Parameter Estimate for _A_1 -0.56 -0.51 8.3% -0.043





11 _A_6 Parameter Estimate for _A_6 0.42 0.48 ( 13%) -0.063






17 d5 Parameter Estimate for d5 11.09 10.94 1.4% 0.150



Page A-154


The CORR Procedure




DTH BMEDD

DTH 1.00000

13

0.98684 <.0001

13

BMEDD 0.98684 <.0001

13

1.00000

61



Page A-155

Brockton Capacity Exempt Volumes Full Model



Dependent Variable DTH



Chow 6 2 9 0.85 0.4573



4 -0.031848 -0.04 0.9685

5 0.047191 0.09 0.9357

6 0.175705 0.38 0.7210

8 0.141688 0.46 0.6634

7 -0.124722 -0.42 0.6880

3 -0.334456 -1.00 0.3449

1 -0.191993 -0.78 0.4531



Error t Value

2 0.656653 0.238497 2.75



Page A-156





SSE 348412271 DFE 10

MSE 34841227 Root MSE 5903

SBC 268.066997 AIC 266.372149

MAE 4509.29263 AICC 269.038816

MAPE 3.94716611 HQC 266.023782





1 0.4107 0.5216 0.0489 0.8250

2 13.0064 0.0015 4.3064 0.1161

3 13.7434 0.0033 4.3084 0.2300

4 14.3591 0.0062 4.5777 0.3334

5 21.0804 0.0008 5.7252 0.3339

6 21.4114 0.0015 5.7739 0.4490

7 21.7638 0.0028 5.7804 0.5656

8 26.7531 0.0008 6.0252 0.6444

9 28.0017 0.0010 6.3885 0.7005

10 37.3852 <.0001 10.7094 0.3806

11 38.9685 <.0001 12.3961 0.3346

12 40.3779 <.0001 13.0000 0.3690

Parameter Estimates



Intercept 1 71248 5227 13.63 <.0001

BMEDD 1 46.0277 3.3296 13.82 <.0001



Page A-157






Page A-158

FULL MODEL FORECAST


Obs pred res Year Q DTH DIFF PCTDIFF SRES

1 137680.13 8491.99 2015 4 148939.8333 11259.70 0.07560 1.43867

2 209328.49 4899.88 2016 1 215825.3333 6496.85 0.03010 0.83012

3 122022.36 2260.88 2016 2 124283.2333 2260.88 0.01819 0.38303

4 68537.95 4357.18 2016 3 72895.13333 4357.18 0.05977 0.73817

5 150237.33 4323.97 2016 4 154561.3 4323.97 0.02798 0.73255

6 215884.16 -794.42 2017 1 215089.7333 -794.42 -0.00369 -0.13459

7 128893.02 -4839.79 2017 2 124053.2333 -4839.79 -0.03901 -0.81994

8 75369.32 5651.01 2017 3 81020.33333 5651.01 0.06975 0.95737

9 146835.13 1771.83 2017 4 148606.9667 1771.83 0.01192 0.30018

10 222087.18 -3680.84 2018 1 218406.3333 -3680.84 -0.01685 -0.62359

11 127552.12 -9109.89 2018 2 118442.2333 -9109.89 -0.07691 -1.54336

12 78104.72 -7237.86 2018 3 70866.86667 -7237.86 -0.10213 -1.22621

13 165799.30 1201.26 2018 4 167000.5667 1201.26 0.00719 0.20351



Page A-159

Brockton Capacity Exempt Volumes Ex Post Model






Page A-160



Obs Year Q DTH xpred EXDIFF EXPCTDIFF

1 2018 1 218406.3333 224834.62 -6428.29 -0.029433

2 2018 2 118442.2333 130242.57 -11800.34 -0.099629

3 2018 3 70866.86667 76386.66 -5519.79 -0.077890

4 2018 4 167000.5667 160571.62 6428.95 0.038497



Page A-161




1 BMEDD Parameter Estimate for BMEDD 46.03 46.11 (.18%) -0.09

2 DTH Parameter Estimate for DTH -1.00 -1.00 .00% 0.00

3 Intercept Intercept Parameter 71247.55 72676.94 (2.0%) -1429.39













Page A-162


The CORR Procedure




CUST POPQ POPQxbefore2013 before2013 Y2016Q1andAFTER Q1Q2 Q4

CUST 1.00000

46

0.97527 <.0001

46

-0.86373 <.0001

46

-0.86908 <.0001

46

0.70967 <.0001

46

0.11518 0.4459

46

0.03916 0.7961

46

POPQ 0.97527 <.0001

46

1.00000

96

-0.81730 <.0001

96

-0.81970 <.0001

96

0.86482 <.0001

96

-0.03475 0.7368

96

0.03013 0.7707

96

POPQxbefore2013 -0.86373 <.0001

46

-0.81730 <.0001

96

1.00000

96

0.99989 <.0001

96

-0.72898 <.0001

96

-0.00102 0.9921

96

0.00090 0.9930

96

before2013 -0.86908 <.0001

46

-0.81970 <.0001

96

0.99989 <.0001

96

1.00000

96

-0.72906 <.0001

96

0.00000 1.0000

96

0.00000 1.0000

96

Y2016Q1andAFTER 0.70967 <.0001

46

0.86482 <.0001

96

-0.72898 <.0001

96

-0.72906 <.0001

96

1.00000

96

-0.02140 0.8360

96

0.01236 0.9049

96

Q1Q2 0.11518 0.4459

46

-0.03475 0.7368

96

-0.00102 0.9921

96

0.00000 1.0000

96

-0.02140 0.8360

96

1.00000

96

-0.57735 <.0001

96

Q4 0.03916 0.7961

46

0.03013 0.7707

96

0.00090 0.9930

96

0.00000 1.0000

96

0.01236 0.9049

96

-0.57735 <.0001

96

1.00000

96



Page A-163

Lawrence Residential Heating Customer Count Full Model






Chow 24 7 32 0.21 0.9796



Error t Value

5 0.468406 0.143325 3.27



Page A-164





SSE 540896.427 DFE 38

MSE 14234 Root MSE 119.30682

SBC 602.767411 AIC 588.13828

MAE 89.2358632 AICC 592.030171

MAPE 0.22453936 HQC 593.61844



Observations 46



1 0.0372 0.8471 0.2500 0.6171

2 1.4865 0.4756 1.4636 0.4810

3 1.5758 0.6649 1.5217 0.6773

4 6.1771 0.1863 4.9362 0.2939

5 13.3601 0.0202 6.9310 0.2258

6 13.3824 0.0374 8.1351 0.2284

7 17.8618 0.0126 10.3058 0.1719

8 18.1760 0.0199 10.7180 0.2182

9 18.2104 0.0328 10.7310 0.2946

10 18.2863 0.0503 11.5296 0.3178

11 18.6385 0.0679 12.7635 0.3091

12 21.5490 0.0429 13.6251 0.3253



Page A-165




Parameter Estimates



Intercept 1 -43801 3778 -11.59 <.0001

POPQ 1 108.7870 4.8949 22.22 <.0001

POPQxbefore2013 1 -22.4601 4.8592 -4.62 <.0001

before2013 1 17099 3746 4.56 <.0001

Y2016Q1andAFTER 1 415.8790 77.2459 5.38 <.0001

Q1Q2 1 1000 38.4874 25.99 <.0001

Q4 1 1141 55.4027 20.60 <.0001

AR5 1 0.9364 0.0420 22.28 <.0001



Page A-166






Page A-167

FULL MODEL FORECAST



1 37406.21 231.151 2007 1 38064.66667 658.454 0.017298 1.93745

2 37443.05 12.973 2007 2 37480 36.954 0.000986 0.10874

3 36509.42 124.126 2007 3 36863 353.584 0.009592 1.04039

4 37732.87 -76.834 2007 4 37514 -218.869 -0.005834 -0.64401

5 37673.25 99.260 2008 1 37956 282.749 0.007449 0.83197

6 37138.71 230.286 2008 2 37369 230.286 0.006162 1.93020

7 36808.90 72.101 2008 3 36881 72.101 0.001955 0.60433

8 37745.66 -73.325 2008 4 37672.33333 -73.325 -0.001946 -0.61459

9 38232.71 89.960 2009 1 38322.66667 89.960 0.002347 0.75402

10 37854.81 32.193 2009 2 37887 32.193 0.000850 0.26983

11 37610.34 -163.008 2009 3 37447.33333 -163.008 -0.004353 -1.36629

12 38503.19 -128.854 2009 4 38374.33333 -128.854 -0.003358 -1.08003

13 38924.11 4.559 2010 1 38928.66667 4.559 0.000117 0.03821

14 38417.79 -27.459 2010 2 38390.33333 -27.459 -0.000715 -0.23015

15 38045.68 -60.676 2010 3 37985 -60.676 -0.001597 -0.50858

16 38910.81 -116.808 2010 4 38794 -116.808 -0.003011 -0.97906

17 39237.08 56.257 2011 1 39293.33333 56.257 0.001432 0.47153

18 38852.19 35.481 2011 2 38887.66667 35.481 0.000912 0.29739

19 38620.94 -103.271 2011 3 38517.66667 -103.271 -0.002681 -0.86559

20 39456.20 -198.867 2011 4 39257.33333 -198.867 -0.005066 -1.66685

21 39870.97 -80.302 2012 1 39790.66667 -80.302 -0.002018 -0.67307

22 39516.14 -26.478 2012 2 39489.66667 -26.478 -0.000671 -0.22193

23 39159.13 150.867 2012 3 39310 150.867 0.003838 1.26453

24 39981.41 77.257 2012 4 40058.66667 77.257 0.001929 0.64755

25 40524.94 -16.937 2013 1 40508 -16.937 -0.000418 -0.14196

26 40203.59 -135.927 2013 2 40067.66667 -135.927 -0.003392 -1.13931

27 39790.09 85.572 2013 3 39875.66667 85.572 0.002146 0.71724

28 40482.98 193.689 2013 4 40676.66667 193.689 0.004762 1.62346

29 41037.98 202.684 2014 1 41240.66667 202.684 0.004915 1.69884

30 40848.75 -33.081 2014 2 40815.66667 -33.081 -0.000810 -0.27728

31 40606.66 -114.662 2014 3 40492 -114.662 -0.002832 -0.96106

32 41322.61 -8.614 2014 4 41314 -8.614 -0.000208 -0.07220

33 41828.71 25.627 2015 1 41854.33333 25.627 0.000612 0.21480

34 41497.26 -67.258 2015 2 41430 -67.258 -0.001623 -0.56374

35 41180.06 -21.395 2015 3 41158.66667 -21.395 -0.000520 -0.17933



Page A-168

FULL MODEL FORECAST



36 41933.34 2.327 2015 4 41935.66667 2.327 0.000055 0.01951

37 42328.08 82.252 2016 1 42410.33333 82.252 0.001939 0.68942

38 42341.92 -102.587 2016 2 42239.33333 -102.587 -0.002429 -0.85986

39 41999.84 128.496 2016 3 42128.33333 128.496 0.003050 1.07702

40 42690.80 -22.801 2016 4 42668 -22.801 -0.000534 -0.19111

41 43102.44 -98.770 2017 1 43003.66667 -98.770 -0.002297 -0.82787

42 42738.41 22.923 2017 2 42761.33333 22.923 0.000536 0.19214

43 42490.65 71.350 2017 3 42562 71.350 0.001676 0.59803

44 43020.78 180.882 2017 4 43201.66667 180.882 0.004187 1.51611

45 43675.19 -134.853 2018 1 43540.33333 -134.853 -0.003097 -1.13030

46 43460.81 -79.809 2018 2 43381 -79.809 -0.001840 -0.66894



Page A-169

Lawrence Residential Heating Customer Count Ex Post forecast






Page A-170



bs YEAR Q CUST XPRED EXDIFF EXPCTDIFF

1 2017 3 42562 42424.28 137.722 0.003235792

2 2017 4 43201.66667 42943.93 257.732 0.005965787

3 2018 1 43540.33333 43639.93 -99.597 -.002287454

4 2018 2 43381 43432.23 -51.226 -.001180831



Page A-171


03:28 Monday, October 28, 2019 10



2 Intercept Intercept Parameter -43800.61 -45588.41 (3.9%) 1787.80

3 POPQ Parameter Estimate for POPQ 108.79 111.08 (2.1%) -2.29

4 POPQxbefore2013 Parameter Estimate for POPQxbefore2013 -22.46 -24.52 (8.4%) 2.06

5 Q1Q2 Parameter Estimate for Q1Q2 1000.39 1028.75 (2.8%) -28.36


7 Y2016Q1andAFTER Parameter Estimate for Y2016Q1andAFTER 415.88 356.95 17% 58.93

8 _A_5 Parameter Estimate for _A_5 0.94 0.95 (1.4%) -0.01




12 before2013 Parameter Estimate for before2013 17098.57 18691.59 (8.5%) -1593.02



Page A-172


The CORR Procedure




RHUPC LMEDDxQ1Q2Q4 ARHHPxQ1Q2Q4

RHUPC 1.00000

66

0.98442 <.0001

66

0.63212 <.0001

66

LMEDDxQ1Q2Q4 0.98442 <.0001

66

1.00000

116

0.74973 <.0001

116

ARHHPxQ1Q2Q4 0.63212 <.0001

66

0.74973 <.0001

116

1.00000

116

LMEDDxy2008Q2andbefore 0.50207 <.0001

66

0.32976 0.0003

116

0.35791 <.0001

116

after2005Q1 -0.13907 0.2654

66

-0.06393 0.4954

116

-0.02812 0.7644

116

Q3 -0.65223 <.0001

66

-0.77199 <.0001

116

-0.96377 <.0001

116

Q4 -0.08742 0.4852

66

0.07390 0.4305

116

0.32047 0.0005

116



LMEDDxy2008Q2andbefore after2005Q1 Q3 Q4

RHUPC 0.50207 <.0001

66

-0.13907 0.2654

66

-0.65223 <.0001

66

-0.08742 0.4852

66

LMEDDxQ1Q2Q4 0.32976 0.0003

116

-0.06393 0.4954

116

-0.77199 <.0001

116

0.07390 0.4305

116

ARHHPxQ1Q2Q4 0.35791 <.0001

116

-0.02812 0.7644

116

-0.96377 <.0001

116

0.32047 0.0005

116

LMEDDxy2008Q2andbefore 1.00000

116

-0.56697 <.0001

116

-0.22947 0.0132

116

-0.01404 0.8811

116



Page A-173


The CORR Procedure




LMEDDxy2008Q2andbefore after2005Q1 Q3 Q4

after2005Q1 -0.56697 <.0001

116

1.00000

116

0.01578 0.8665

116

0.01578 0.8665

116

Q3 -0.22947 0.0132

116

0.01578 0.8665

116

1.00000

116

-0.33333 0.0003

116

Q4 -0.01404 0.8811

116

0.01578 0.8665

116

-0.33333 0.0003

116

1.00000

116



Page A-174

Lawrence Residential Heating UPC Full Model






Chow 20 7 52 2.03 0.0686



6 -0.007549 -0.05 0.9582

5 -0.011684 -0.09 0.9326

2 0.078284 0.57 0.5729

3 0.134370 1.01 0.3163

7 -0.135046 -1.02 0.3117

8 0.114357 0.89 0.3776

4 -0.146248 -1.15 0.2543



Error t Value

1 -0.257672 0.126873 -2.03



Page A-175





SSE 3.38541362 DFE 58

MSE 0.05837 Root MSE 0.24160

SBC 24.8774938 AIC 7.36025588

MAE 0.1705127 AICC 9.88657167

MAPE 2.45928273 HQC 14.2821492

Log Likelihood 4.31987206 Transformed Regression R-Square 0.9987


Observations 66



1 3.2172 0.0729 1.6565 0.1981

2 7.8863 0.0194 4.7857 0.0914

3 11.9135 0.0077 6.4099 0.0933

4 12.1178 0.0165 6.8214 0.1456

5 14.3179 0.0137 6.9577 0.2238

6 14.9427 0.0207 7.8463 0.2496

7 15.5762 0.0293 7.8548 0.3456

8 16.1099 0.0408 7.9694 0.4365

9 16.1894 0.0630 7.9695 0.5372

10 16.9012 0.0766 8.8660 0.5449

11 17.0703 0.1058 8.8661 0.6343

12 17.1504 0.1440 8.9720 0.7053



Page A-176




Parameter Estimates



Intercept 1 0.8529 0.2894 2.95 0.0046

LMEDDxQ1Q2Q4 1 0.005029 0.0000361 139.43 <.0001

ARHHPxQ1Q2Q4 1 -0.0312 0.0189 -1.66 0.1032

LMEDDxy2008Q2andbefore 1 0.000337 0.0000437 7.73 <.0001

after2005Q1 1 -0.4050 0.1226 -3.30 0.0016

Q3 1 1.7622 0.3023 5.83 <.0001

Q4 1 -1.0468 0.0745 -14.05 <.0001

AR1 1 -0.2967 0.1271 -2.33 0.0231



Page A-177






Page A-178

FULL MODEL FORECAST



1 17.0437 0.04531 2002 1 17.0911315 0.04745 0.002776 0.18756

2 8.2537 -0.20398 2002 2 8.0496915 -0.20398 -0.025340 -0.84430

3 2.5951 -0.05348 2002 3 2.541569455 -0.05348 -0.021044 -0.22138

4 10.5845 0.25478 2002 4 10.83930255 0.25478 0.023505 1.05455

5 22.1594 0.04516 2003 1 22.20455178 0.04516 0.002034 0.18691

6 9.0301 -0.34786 2003 2 8.6822083 -0.34786 -0.040066 -1.43985

7 2.5540 -0.03555 2003 3 2.518427698 -0.03555 -0.014115 -0.14713

8 9.5308 0.26527 2003 4 9.796111001 0.26527 0.027079 1.09798

9 20.3809 0.59670 2004 1 20.97759335 0.59670 0.028445 2.46981

10 8.2167 0.03198 2004 2 8.248658384 0.03198 0.003877 0.13236

11 2.7252 -0.14679 2004 3 2.578405696 -0.14679 -0.056931 -0.60758

12 9.6677 -0.56935 2004 4 9.098344341 -0.56935 -0.062577 -2.35660

13 20.1245 0.09376 2005 1 20.21824247 0.09376 0.004637 0.38808

14 8.5697 -0.01865 2005 2 8.551031077 -0.01865 -0.002181 -0.07718

15 2.2246 0.20590 2005 3 2.430479771 0.20590 0.084717 0.85225

16 8.4654 0.23695 2005 4 8.702376151 0.23695 0.027228 0.98078

17 18.2188 -0.08014 2006 1 18.1386365 -0.08014 -0.004418 -0.33171

18 7.2519 -0.09978 2006 2 7.152108555 -0.09978 -0.013951 -0.41301

19 2.2234 0.14193 2006 3 2.365322383 0.14193 0.060002 0.58744

20 7.4537 -0.14189 2006 4 7.31178051 -0.14189 -0.019406 -0.58731

21 19.3421 -0.39585 2007 1 18.94625812 -0.39585 -0.020893 -1.63848

22 7.6559 0.30276 2007 2 7.95861793 0.30276 0.038042 1.25316

23 2.3071 0.07359 2007 3 2.380725027 0.07359 0.030912 0.30461

24 8.8197 -0.08858 2007 4 8.731095946 -0.08858 -0.010145 -0.36665

25 18.6805 -0.25036 2008 1 18.43012962 -0.25036 -0.013584 -1.03629

26 7.4999 0.10008 2008 2 7.599953616 0.10008 0.013169 0.41425

27 2.2453 0.06340 2008 3 2.308677458 0.06340 0.027460 0.26241

28 8.4553 -0.03068 2008 4 8.424635232 -0.03068 -0.003642 -0.12701

29 19.0100 0.46151 2009 1 19.47153121 0.46151 0.023702 1.91024

30 6.6859 0.08955 2009 2 6.775455081 0.08955 0.013217 0.37068

31 2.2770 0.18611 2009 3 2.463139342 0.18611 0.075559 0.77034

32 8.1077 -0.15134 2009 4 7.956377092 -0.15134 -0.019021 -0.62640

33 17.6000 0.39509 2010 1 17.99511928 0.39509 0.021955 1.63533

34 5.5930 0.33276 2010 2 5.925762562 0.33276 0.056155 1.37735

35 2.3416 -0.19095 2010 3 2.150664736 -0.19095 -0.088785 -0.79036



Page A-179

FULL MODEL FORECAST



36 8.5203 0.13064 2010 4 8.650933993 0.13064 0.015102 0.54075

37 18.8364 0.17012 2011 1 19.0065151 0.17012 0.008950 0.70413

38 7.2635 0.02699 2011 2 7.290477701 0.02699 0.003702 0.11172

39 2.2360 -0.00459 2011 3 2.231434926 -0.00459 -0.002056 -0.01899

40 6.7465 0.33368 2011 4 7.080222803 0.33368 0.047128 1.38113

41 15.7610 -0.06495 2012 1 15.69602587 -0.06495 -0.004138 -0.26882

42 5.6736 -0.03620 2012 2 5.637407254 -0.03620 -0.006421 -0.14983

43 2.2025 -0.08764 2012 3 2.11486475 -0.08764 -0.041441 -0.36276

44 7.9542 -0.11312 2012 4 7.841066436 -0.11312 -0.014427 -0.46822

45 17.6310 -0.02053 2013 1 17.61048023 -0.02053 -0.001166 -0.08497

46 7.3736 -0.35517 2013 2 7.018418841 -0.35517 -0.050605 -1.47007

47 2.0992 0.10203 2013 3 2.201225476 0.10203 0.046350 0.42230

48 8.9402 -0.36803 2013 4 8.572187167 -0.36803 -0.042933 -1.52332

49 19.8448 0.11387 2014 1 19.95864115 0.11387 0.005706 0.47134

50 7.6714 -0.00424 2014 2 7.667137619 -0.00424 -0.000552 -0.01753

51 2.2092 -0.01273 2014 3 2.196442096 -0.01273 -0.005795 -0.05268

52 8.1130 -0.01959 2014 4 8.093382389 -0.01959 -0.002420 -0.08107

53 21.1572 -0.38161 2015 1 20.77561861 -0.38161 -0.018368 -1.57954

54 7.2318 0.14311 2015 2 7.374945692 0.14311 0.019405 0.59235

55 2.2183 -0.08168 2015 3 2.136650037 -0.08168 -0.038229 -0.33809

56 6.9963 -0.13650 2015 4 6.859761381 -0.13650 -0.019899 -0.56501

57 16.0464 -0.44283 2016 1 15.6035243 -0.44283 -0.028380 -1.83292

58 6.9276 -0.09175 2016 2 6.835887561 -0.09175 -0.013422 -0.37978

59 2.1397 -0.08700 2016 3 2.052735689 -0.08700 -0.042383 -0.36011

60 8.0265 -0.16386 2016 4 7.862660542 -0.16386 -0.020840 -0.67823

61 16.6801 -0.21129 2017 1 16.46885149 -0.21129 -0.012829 -0.87454

62 7.5295 0.10632 2017 2 7.635815846 0.10632 0.013924 0.44007

63 2.2175 -0.07022 2017 3 2.147306674 -0.07022 -0.032702 -0.29066

64 7.2171 0.56947 2017 4 7.786543729 0.56947 0.073135 2.35710

65 18.2062 -0.00220 2018 1 18.20396414 -0.00220 -0.000121 -0.00910

66 7.3705 -0.07406 2018 2 7.296420092 -0.07406 -0.010151 -0.30656



Page A-180

Lawrence Residential Heating UPC Ex Post Model






Page A-181


03:30 Monday, October 28, 2019 10

Obs YEAR Q RHUPC XPRED EXDIFF EXPCTDIFF

1 2017 3 2.147306674 2.2270 -0.07966 -0.037096

2 2017 4 7.786543729 7.1785 0.60809 0.078095

3 2018 1 18.20396414 18.0246 0.17938 0.009854

4 2018 2 7.296420092 7.3193 -0.02286 -0.003134



Page A-182


03:30 Monday, October 28, 2019 11


1 ARHHPxQ1Q2Q4 Parameter Estimate for ARHHPxQ1Q2Q4 -0.03122 -0.02244 39% -0.00878


3 LMEDDxQ1Q2Q4 Parameter Estimate for LMEDDxQ1Q2Q4 0.00503 0.00503 .06% 0.00000

4 LMEDDxy2008Q2andbefore Parameter Estimate for LMEDDxy2008Q2andbefore 0.00034 0.00033 .86% 0.00000












16 _LIKLHD_ Log-Likelihood 4.31987 5.71103 ( 24%) -1.39116



19 after2005Q1 Parameter Estimate for after2005Q1 -0.40498 -0.42453 (4.6%) 0.01955



Page A-183

The CORR Procedure




CUST TREND DQ116A DQ114A TrendxDQ114A DQ29 DQ112

CUST 1.00000

50

-0.99549 <.0001

50

-0.69899 <.0001

50

-0.86196 <.0001

50

-0.87999 <.0001

50

0.09674 0.5039

50

-0.00038 0.9979

50

TREND -0.99549 <.0001

50

1.00000

100

0.84857 <.0001

100

0.80800 <.0001

100

0.97573 <.0001

100

-0.12708 0.2077

100

-0.08878 0.3797

100

DQ116A -0.69899 <.0001

50

0.84857 <.0001

100

1.00000

100

0.84017 <.0001

100

0.88489 <.0001

100

-0.12309 0.2224

100

-0.12309 0.2224

100

DQ114A -0.86196 <.0001

50

0.80800 <.0001

100

0.84017 <.0001

100

1.00000

100

0.88658 <.0001

100

-0.14651 0.1458

100

-0.14651 0.1458

100

TrendxDQ114A -0.87999 <.0001

50

0.97573 <.0001

100

0.88489 <.0001

100

0.88658 <.0001

100

1.00000

100

-0.12989 0.1977

100

-0.12989 0.1977

100

DQ29 0.09674 0.5039

50

-0.12708 0.2077

100

-0.12309 0.2224

100

-0.14651 0.1458

100

-0.12989 0.1977

100

1.00000

100

-0.01010 0.9205

100

DQ112 -0.00038 0.9979

50

-0.08878 0.3797

100

-0.12309 0.2224

100

-0.14651 0.1458

100

-0.12989 0.1977

100

-0.01010 0.9205

100

1.00000

100

TrendxDQ116A -0.69992 <.0001

50

0.95925 <.0001

100

0.93216 <.0001

100

0.78317 <.0001

100

0.96366 <.0001

100

-0.11474 0.2556

100

-0.11474 0.2556

100

DQ39 0.08798 0.5435

50

-0.12360 0.2205

100

-0.12309 0.2224

100

-0.14651 0.1458

100

-0.12989 0.1977

100

-0.01010 0.9205

100

-0.01010 0.9205

100

DQ314 -0.09966 0.4911

50

-0.05049 0.6179

100

-0.12309 0.2224

100

0.06895 0.4955

100

-0.02648 0.7937

100

-0.01010 0.9205

100

-0.01010 0.9205

100

Q1 0.00581 0.9680

50

-0.03000 0.7670

100

0.00000 1.0000

100

0.00000 1.0000

100

-0.01683 0.8680

100

-0.05803 0.5663

100

0.17408 0.0832

100



TrendxDQ116A DQ39 DQ314 Q1

CUST -0.69992 <.0001

50

0.08798 0.5435

50

-0.09966 0.4911

50

0.00581 0.9680

50



Page A-184

The CORR Procedure




TrendxDQ116A DQ39 DQ314 Q1

TREND 0.95925 <.0001

100

-0.12360 0.2205

100

-0.05049 0.6179

100

-0.03000 0.7670

100

DQ116A 0.93216 <.0001

100

-0.12309 0.2224

100

-0.12309 0.2224

100

0.00000 1.0000

100

DQ114A 0.78317 <.0001

100

-0.14651 0.1458

100

0.06895 0.4955

100

0.00000 1.0000

100

TrendxDQ114A 0.96366 <.0001

100

-0.12989 0.1977

100

-0.02648 0.7937

100

-0.01683 0.8680

100

DQ29 -0.11474 0.2556

100

-0.01010 0.9205

100

-0.01010 0.9205

100

-0.05803 0.5663

100

DQ112 -0.11474 0.2556

100

-0.01010 0.9205

100

-0.01010 0.9205

100

0.17408 0.0832

100

TrendxDQ116A 1.00000

100

-0.11474 0.2556

100

-0.11474 0.2556

100

-0.01402 0.8899

100

DQ39 -0.11474 0.2556

100

1.00000

100

-0.01010 0.9205

100

-0.05803 0.5663

100

DQ314 -0.11474 0.2556

100

-0.01010 0.9205

100

1.00000

100

-0.05803 0.5663

100

Q1 -0.01402 0.8899

100

-0.05803 0.5663

100

-0.05803 0.5663

100

1.00000

100



Page A-185

Lawrence Residential Non-Heat Customer Count Full Model






Chow 20 11 28 0.65 0.7721



7 -0.024790 -0.12 0.9047

5 -0.031832 -0.16 0.8769

4 0.065529 0.37 0.7126

8 0.114488 0.81 0.4224

6 0.137448 0.95 0.3485



Error t Value

1 -0.560035 0.153907 -3.64

2 0.395920 0.167468 2.36

3 -0.383732 0.153907 -2.49



Page A-186





SSE 13889.7745 DFE 36

MSE 385.82707 Root MSE 19.64248

SBC 478.745941 AIC 451.977619

MAE 13.3427907 AICC 463.977619

MAPE 0.39567788 HQC 462.171149





1 1.8968 0.1684 0.7659 0.3815

2 4.1448 0.1259 1.7564 0.4155

3 7.6488 0.0539 4.5260 0.2100

4 7.7523 0.1011 4.5262 0.3395

5 22.0362 0.0005 9.5022 0.0906

6 22.0516 0.0012 9.6327 0.1410

7 22.9799 0.0017 11.0906 0.1347

8 23.4803 0.0028 11.7299 0.1637

9 23.9247 0.0044 11.9252 0.2176

10 25.0152 0.0053 11.9292 0.2898

11 25.2873 0.0083 12.3513 0.3378

12 28.7696 0.0043 12.5655 0.4014



Page A-187




Parameter Estimates



Intercept 1 4264 13.8173 308.61 <.0001

TREND 1 -34.4892 0.6965 -49.52 <.0001

DQ116A 1 -1339 210.2081 -6.37 <.0001

DQ114A 1 422.0337 139.2523 3.03 0.0045

TrendxDQ114A 1 -15.4745 3.9426 -3.92 0.0004

DQ29 1 -65.3934 19.3443 -3.38 0.0018

DQ112 1 -45.3028 16.3408 -2.77 0.0088

TrendxDQ116A 1 32.8147 5.2233 6.28 <.0001

DQ39 1 -64.2371 19.1324 -3.36 0.0019

DQ314 1 43.0782 16.1795 2.66 0.0115

Q1 1 -16.0325 5.2845 -3.03 0.0045



Page A-188






Page A-189

FULL MODEL FORECAST


Obs pred res Year Q CUST DIFF PCTDIFF SRES

1 4213.65 6.6450 2006 1 4221.666667 8.0146 0.001898 0.33829

2 4198.36 -18.6730 2006 2 4177.666667 -20.6926 -0.004953 -0.95064

3 4150.62 -1.4922 2006 3 4149 -1.6159 -0.000389 -0.07597

4 4129.68 22.9902 2006 4 4152.666667 22.9902 0.005536 1.17043

5 4088.42 7.2504 2007 1 4095.666667 7.2504 0.001770 0.36912

6 4053.46 -16.1258 2007 2 4037.333333 -16.1258 -0.003994 -0.82096

7 4013.84 9.4892 2007 3 4023.333333 9.4892 0.002359 0.48310

8 4004.13 21.5351 2007 4 4025.666667 21.5351 0.005349 1.09635

9 3950.82 19.1822 2008 1 3970 19.1822 0.004832 0.97657

10 3922.76 21.2367 2008 2 3944 21.2367 0.005385 1.08116

11 3900.22 3.4501 2008 3 3903.666667 3.4501 0.000884 0.17565

12 3863.47 25.5334 2008 4 3889 25.5334 0.006566 1.29990

13 3823.47 -29.1321 2009 1 3794.333333 -29.1321 -0.007678 -1.48312

14 3704.80 -3.4685 2009 2 3701.333333 -3.4685 -0.000937 -0.17658

15 3691.43 -23.7625 2009 3 3667.666667 -23.7625 -0.006479 -1.20975

16 3707.67 -30.6729 2009 4 3677 -30.6729 -0.008342 -1.56156

17 3642.34 -6.0060 2010 1 3636.333333 -6.0060 -0.001652 -0.30577

18 3637.36 -23.3562 2010 2 3614 -23.3562 -0.006463 -1.18907

19 3588.96 -18.2938 2010 3 3570.666667 -18.2938 -0.005123 -0.93134

20 3554.83 16.8319 2010 4 3571.666667 16.8319 0.004713 0.85692

21 3526.20 -27.5355 2011 1 3498.666667 -27.5355 -0.007870 -1.40184

22 3477.71 2.6212 2011 2 3480.333333 2.6212 0.000753 0.13344

23 3465.81 0.8567 2011 3 3466.666667 0.8567 0.000247 0.04361

24 3434.31 13.3590 2011 4 3447.666667 13.3590 0.003875 0.68011

25 3338.96 -10.9610 2012 1 3328 -10.9610 -0.003294 -0.55802

26 3354.31 -39.9790 2012 2 3314.333333 -39.9790 -0.012062 -2.03533

27 3312.52 -11.5179 2012 3 3301 -11.5179 -0.003489 -0.58638

28 3296.77 17.8992 2012 4 3314.666667 17.8992 0.005400 0.91125

29 3249.29 26.0407 2013 1 3275.333333 26.0407 0.007951 1.32573

30 3226.15 8.8459 2013 2 3235 8.8459 0.002734 0.45035

31 3193.46 34.8709 2013 3 3228.333333 34.8709 0.010802 1.77528

32 3187.51 3.1571 2013 4 3190.666667 3.1571 0.000989 0.16073

33 3027.17 -13.1719 2014 1 3014 -13.1719 -0.004370 -0.67058

34 2984.16 1.1701 2014 2 2985.333333 1.1701 0.000392 0.05957

35 2950.78 20.8837 2014 3 2971.666667 20.8837 0.007028 1.06319



Page A-190

FULL MODEL FORECAST



36 2947.74 -1.4101 2014 4 2946.333333 -1.4101 -0.000479 -0.07179

37 2815.99 -0.6538 2015 1 2815.333333 -0.6538 -0.000232 -0.03328

38 2791.01 -5.0089 2015 2 2786 -5.0089 -0.001798 -0.25501

39 2745.22 -7.8886 2015 3 2737.333333 -7.8886 -0.002882 -0.40161

40 2685.75 10.5827 2015 4 2696.333333 10.5827 0.003925 0.53877

41 2632.23 5.4339 2016 1 2637.666667 5.4339 0.002060 0.27664

42 2628.69 -18.6934 2016 2 2610 -18.6934 -0.007162 -0.95168

43 2599.67 -1.0041 2016 3 2598.666667 -1.0041 -0.000386 -0.05112

44 2596.72 3.2789 2016 4 2600 3.2789 0.001261 0.16693

45 2561.40 -2.0663 2017 1 2559.333333 -2.0663 -0.000807 -0.10519

46 2551.01 -5.3386 2017 2 2545.666667 -5.3386 -0.002097 -0.27179

47 2536.88 4.1165 2017 3 2541 4.1165 0.001620 0.20957

48 2528.82 17.8451 2017 4 2546.666667 17.8451 0.007007 0.90849

49 2498.66 8.0069 2018 1 2506.666667 8.0069 0.003194 0.40763

50 2489.48 -17.8146 2018 2 2471.666667 -17.8146 -0.007208 -0.90694



Page A-191

Lawrence Residential Non-Heat Customer Count EX POST Model






Page A-192


03:31 Monday, October 28, 2019 10

Obs Year Q CUST XPRED EXDIFF EXPCTDIFF

1 2017 3 2541 2530.70 10.3015 0.004054

2 2017 4 2546.666667 2516.98 29.6828 0.011656

3 2018 1 2506.666667 2477.63 29.0411 0.011586

4 2018 2 2471.666667 2473.50 -1.8360 -0.000743



Page A-193


03:31 Monday, October 28, 2019 11



2 DQ112 Parameter Estimate for DQ112 -45.30 -43.86 3.3% -1.439

3 DQ114A Parameter Estimate for DQ114A 422.03 426.24 (.99%) -4.204

4 DQ116A Parameter Estimate for DQ116A -1339.20 -1265.78 5.8% -73.412

5 DQ29 Parameter Estimate for DQ29 -65.39 -65.62 (.35%) 0.231

6 DQ314 Parameter Estimate for DQ314 43.08 41.88 2.9% 1.196




10 TREND Parameter Estimate for TREND -34.49 -34.48 .02% -0.005

11 TrendxDQ114A Parameter Estimate for TrendxDQ114A -15.47 -15.60 (.78%) 0.122

12 TrendxDQ116A Parameter Estimate for TrendxDQ116A 32.81 31.09 5.6% 1.727


14 _A_2 Parameter Estimate for _A_2 0.40 0.39 .36% 0.001







21 _MSE_ Estimate of Variance 385.83 407.14 (5.2%) -21.316




Page A-194


The CORR Procedure




UPC Q1xLMEDD Q2xLMEDD Q4xLMEDD DQ113 DQ215 ARNHP

UPC 1.00000

50

0.80972 <.0001

50

-0.01449 0.9205

50

-0.04146 0.7750

50

0.34747 0.0134

50

-0.12790 0.3761

50

-0.07960 0.5827

50

Q1xLMEDD 0.80972 <.0001

50

1.00000

100

-0.33135 0.0008

100

-0.33150 0.0008

100

0.17341 0.0845

100

-0.05388 0.5945

100

0.02738 0.7868

100

Q2xLMEDD -0.01449 0.9205

50

-0.33135 0.0008

100

1.00000

100

-0.33132 0.0008

100

-0.05784 0.5676

100

0.01654 0.8703

100

0.01520 0.8807

100

Q4xLMEDD -0.04146 0.7750

50

-0.33150 0.0008

100

-0.33132 0.0008

100

1.00000

100

-0.05786 0.5674

100

0.02144 0.8323

100

-0.02826 0.7802

100

DQ113 0.34747 0.0134

50

0.17341 0.0845

100

-0.05784 0.5676

100

-0.05786 0.5674

100

1.00000

100

-0.13115 0.1934

100

-0.16031 0.1111

100

DQ215 -0.12790 0.3761

50

-0.05388 0.5945

100

0.01654 0.8703

100

0.02144 0.8323

100

-0.13115 0.1934

100

1.00000

100

-0.35945 0.0002

100

ARNHP -0.07960 0.5827

50

0.02738 0.7868

100

0.01520 0.8807

100

-0.02826 0.7802

100

-0.16031 0.1111

100

-0.35945 0.0002

100

1.00000

100



Page A-195

Lawrence Residential Non-Heating UPC Full Model






Chow 20 7 36 1.31 0.2721



3 0.008759 0.05 0.9588

4 -0.051884 -0.33 0.7449

1 0.075797 0.47 0.6432

6 0.086588 0.56 0.5770

7 0.105479 0.66 0.5100

2 0.109189 0.73 0.4702

8 -0.128226 -0.86 0.3923




Error t Value

5 0.285616 0.147876 1.93



Page A-196





SSE 0.21322868 DFE 42

MSE 0.00508 Root MSE 0.07125

SBC -99.255135 AIC -114.55132

MAE 0.05059575 AICC -111.03912

MAPE 3.16999827 HQC -108.72644





1 3.0916 0.0787 1.3448 0.2462

2 4.2473 0.1196 2.4371 0.2957

3 4.2475 0.2359 2.5846 0.4602

4 4.7810 0.3105 3.1408 0.5345

5 5.0499 0.4098 3.1848 0.6715

6 5.0775 0.5339 3.2429 0.7778

7 5.2917 0.6244 3.2855 0.8574

8 6.1566 0.6297 3.7931 0.8753

9 9.7202 0.3736 7.1071 0.6260

10 9.7202 0.4654 7.2878 0.6980

11 12.5634 0.3228 8.8785 0.6331

12 12.5637 0.4015 9.9872 0.6171



Page A-197




Parameter Estimates



Intercept 1 1.6476 0.0921 17.89 <.0001

Q1xLMEDD 1 0.000334 7.7928E-6 42.86 <.0001

Q2xLMEDD 1 0.000434 0.0000236 18.36 <.0001

Q4xLMEDD 1 0.000323 0.0000189 17.08 <.0001

DQ113 1 0.4787 0.0741 6.46 <.0001

DQ215 1 -0.1159 0.0206 -5.64 <.0001

ARNHP 1 -0.0232 0.004240 -5.48 <.0001



Page A-198






Page A-199

FULL MODEL FORECAST



1 2.19537 0.00110 2006 1 2.196525858 0.00115 0.00052 0.01549

2 1.64572 0.05805 2006 2 1.70629538 0.06058 0.03550 0.81475

3 1.03088 0.11477 2006 3 1.150638708 0.11976 0.10408 1.61073

4 1.56995 0.02012 2006 4 1.590945577 0.02100 0.01320 0.28240

5 2.29244 -0.08394 2007 1 2.204850655 -0.08759 -0.03973 -1.17811

6 1.73446 -0.00848 2007 2 1.72597424 -0.00848 -0.00491 -0.11905

7 1.08920 0.05562 2007 3 1.144821873 0.05562 0.04858 0.78059

8 1.68473 -0.07050 2007 4 1.614225387 -0.07050 -0.04368 -0.98948

9 2.29735 -0.04378 2008 1 2.25356843 -0.04378 -0.01943 -0.61451

10 1.76536 -0.05745 2008 2 1.707910751 -0.05745 -0.03364 -0.80634

11 1.10255 0.02920 2008 3 1.131756468 0.02920 0.02580 0.40985

12 1.69829 -0.01302 2008 4 1.685266135 -0.01302 -0.00773 -0.18278

13 2.39355 -0.12903 2009 1 2.264517263 -0.12903 -0.05698 -1.81087

14 1.71019 0.04359 2009 2 1.753782421 0.04359 0.02486 0.61182

15 1.16736 0.07421 2009 3 1.241570481 0.07421 0.05977 1.04149

16 1.75828 -0.06587 2009 4 1.692412293 -0.06587 -0.03892 -0.92444

17 2.36742 0.04976 2010 1 2.417178477 0.04976 0.02058 0.69832

18 1.69395 -0.00302 2010 2 1.690924184 -0.00302 -0.00179 -0.04242

19 1.16292 -0.06033 2010 3 1.10259522 -0.06033 -0.05472 -0.84671

20 1.76215 0.02245 2010 4 1.784601026 0.02245 0.01258 0.31512

21 2.44992 0.00968 2011 1 2.459603658 0.00968 0.00394 0.13585

22 1.78357 0.02564 2011 2 1.809213677 0.02564 0.01417 0.35984

23 1.17577 -0.02058 2011 3 1.155192308 -0.02058 -0.01781 -0.28883

24 1.71459 0.07937 2011 4 1.793966934 0.07937 0.04424 1.11397

25 2.24547 0.03598 2012 1 2.281450321 0.03598 0.01577 0.50504

26 1.69464 -0.02221 2012 2 1.672432868 -0.02221 -0.01328 -0.31167

27 1.21897 -0.17504 2012 3 1.043926083 -0.17504 -0.16768 -2.45669

28 1.81723 0.03725 2012 4 1.854485116 0.03725 0.02009 0.52284

29 2.85530 0.00070 2013 1 2.855994301 0.00070 0.00024 0.00980

30 1.85305 0.07636 2013 2 1.929417826 0.07636 0.03958 1.07174

31 1.23295 -0.08809 2013 3 1.144863191 -0.08809 -0.07694 -1.23632

32 1.91266 0.06091 2013 4 1.973568742 0.06091 0.03086 0.85489

33 2.51633 0.10145 2014 1 2.617783676 0.10145 0.03876 1.42388

34 1.85171 -0.00245 2014 2 1.849263064 -0.00245 -0.00132 -0.03432

35 1.15526 0.04351 2014 3 1.198766124 0.04351 0.03629 0.61063



Page A-200

FULL MODEL FORECAST



36 1.76963 0.00500 2014 4 1.77463514 0.00500 0.00282 0.07020

37 2.51540 -0.08218 2015 1 2.433222828 -0.08218 -0.03377 -1.15338

38 1.65138 0.08719 2015 2 1.738573822 0.08719 0.05015 1.22367

39 1.04297 0.04215 2015 3 1.085119338 0.04215 0.03885 0.59160

40 1.56561 -0.01116 2015 4 1.55445667 -0.01116 -0.00718 -0.15660

41 2.13723 0.01176 2016 1 2.148995324 0.01176 0.00547 0.16507

42 1.72528 -0.08824 2016 2 1.637037037 -0.08824 -0.05390 -1.23847

43 1.06872 -0.02678 2016 3 1.041944587 -0.02678 -0.02570 -0.37585

44 1.65088 -0.09152 2016 4 1.559358974 -0.09152 -0.05869 -1.28440

45 2.18810 -0.01996 2017 1 2.168142746 -0.01996 -0.00921 -0.28011

46 1.72548 -0.02730 2017 2 1.698179913 -0.02730 -0.01608 -0.38318

47 1.09408 0.01309 2017 3 1.107175652 0.01309 0.01183 0.18376

48 1.61903 0.00440 2017 4 1.623429319 0.00440 0.00271 0.06169

49 2.28241 0.17557 2018 1 2.457978723 0.17557 0.07143 2.46400

50 1.66776 -0.05993 2018 2 1.607821982 -0.05993 -0.03728 -0.84116



Page A-201

Lawrence Residential Non-Heating UPC Ex Post Model






Page A-202




1 2017 3 1.107175652 1.05694 0.05023 0.045368

2 2017 4 1.623429319 1.58606 0.03737 0.023019

3 2018 1 2.457978723 2.22043 0.23755 0.096643

4 2018 2 1.607821982 1.66201 -0.05419 -0.033703



Page A-203


03:33 Monday, October 28, 2019 10


1 ARNHP Parameter Estimate for ARNHP -0.02323 -0.02184 6.4% -0.001392

2 DQ113 Parameter Estimate for DQ113 0.47872 0.46733 2.4% 0.011386

3 DQ215 Parameter Estimate for DQ215 -0.11590 -0.13513 ( 14%) 0.019225


5 Q1xLMEDD Parameter Estimate for Q1xLMEDD 0.00033 0.00033 1.1% 0.000004

6 Q2xLMEDD Parameter Estimate for Q2xLMEDD 0.00043 0.00045 (2.6%) -0.000011

7 Q4xLMEDD Parameter Estimate for Q4xLMEDD 0.00032 0.00032 (.17%) -0.000001














Page A-204


The CORR Procedure




cust Q1 Q3 Q4 enm ENMD1 ENMD2

cust LAWRENCE COMMERCIAL & INDUSTRIAL LOW CUSTOMER COUNT

1.00000

46

0.33323 0.0236

46

-0.36888 0.0116

46

0.08549 0.5721

46

0.85838 <.0001

46

-0.55488 <.0001

46

0.02643 0.8616

46


0.33323 0.0236

46

1.00000

96

-0.33333 0.0009

96

-0.33333 0.0009

96

-0.02547 0.8055

96

-0.00012 0.9991

96

0.03368 0.7446

96


-0.36888 0.0116

46

-0.33333 0.0009

96

1.00000

96

-0.33333 0.0009

96

0.00849 0.9345

96

0.00010 0.9992

96

0.03537 0.7323

96


0.08549 0.5721

46

-0.33333 0.0009

96

-0.33333 0.0009

96

1.00000

96

0.02539 0.8061

96

-0.00015 0.9988

96

-0.10369 0.3147

96


0.85838 <.0001

46

-0.02547 0.8055

96

0.00849 0.9345

96

0.02539 0.8061

96

1.00000

96

-0.47529 <.0001

96

-0.19799 0.0532

96

ENMD1 INTERACTION TERM FOR NON MFG EMPLOYMENT BETWEEN 2007Q1 AND 2008Q4

-0.55488 <.0001

46

-0.00012 0.9991

96

0.00010 0.9992

96

-0.00015 0.9988

96

-0.47529 <.0001

96

1.00000

96

-0.05415 0.6003

96


0.02643 0.8616

46

0.03368 0.7446

96

0.03537 0.7323

96

-0.10369 0.3147

96

-0.19799 0.0532

96

-0.05415 0.6003

96

1.00000

96


0.41772 0.0039

46

-0.00020 0.9984

96

0.00062 0.9953

96

-0.00069 0.9947

96

-0.03483 0.7362

96

-0.06287 0.5428

96

-0.03745 0.7172

96


0.24386 0.1024

46

0.08440 0.4136

96

-0.08422 0.4146

96

0.08403 0.4156

96

-0.00708 0.9454

96

-0.04398 0.6705

96

-0.02620 0.8000

96

D2015Q1 INTERCEPT DUMMY YEAR 2015 QUARTER 1

0.23589 0.1145

46

0.17770 0.0832

96

-0.05923 0.5665

96

-0.05923 0.5665

96

-0.04403 0.6701

96

-0.03093 0.7648

96

-0.01843 0.8586

96



ENMD3 ENMD4 D2015Q1

cust LAWRENCE COMMERCIAL & INDUSTRIAL LOW CUSTOMER COUNT

0.41772 0.0039

46

0.24386 0.1024

46

0.23589 0.1145

46


-0.00020 0.9984

96

0.08440 0.4136

96

0.17770 0.0832

96



Page A-205


The CORR Procedure




ENMD3 ENMD4 D2015Q1


0.00062 0.9953

96

-0.08422 0.4146

96

-0.05923 0.5665

96


-0.00069 0.9947

96

0.08403 0.4156

96

-0.05923 0.5665

96


-0.03483 0.7362

96

-0.00708 0.9454

96

-0.04403 0.6701

96


-0.06287 0.5428

96

-0.04398 0.6705

96

-0.03093 0.7648

96


-0.03745 0.7172

96

-0.02620 0.8000

96

-0.01843 0.8586

96


1.00000

96

-0.03041 0.7686

96

-0.02139 0.8361

96


-0.03041 0.7686

96

1.00000

96

-0.01497 0.8849

96


-0.02139 0.8361

96

-0.01497 0.8849

96

1.00000

96



Page A-206

LAWRENCE COMMERCIAL & INDUSTRIAL LOW CUSTOMER COUNT REGRESSION





Chow 28 10 26 0.97 0.4920



5 -0.018221 -0.09 0.9270

3 0.078757 0.42 0.6802

8 0.092059 0.51 0.6163

6 0.071848 0.44 0.6646

2 0.230988 1.46 0.1551

4 0.271342 1.78 0.0839

7 0.246759 1.60 0.1186




Error t Value

1 -0.401924 0.154777 -2.60

Algorithm converged.


SSE 9372.77054 DFE 35

MSE 267.79344 Root MSE 16.36440

SBC 417.53788 AIC 397.422825

MAE 11.3504289 AICC 405.187531

MAPE 0.40600492 HQC 404.958045




Page A-207






Observations 46



1 0.8042 0.3698 0.5845 0.4445

2 2.8380 0.2420 1.8863 0.3894

3 3.2383 0.3563 2.3767 0.4980

4 3.2384 0.5188 2.5544 0.6349

5 3.6374 0.6027 3.1151 0.6823

6 5.7999 0.4460 4.7455 0.5768

7 5.8939 0.5522 4.8930 0.6730

8 5.9217 0.6560 5.8520 0.6638

9 6.6903 0.6693 5.8524 0.7546

10 6.7757 0.7464 6.2102 0.7973

11 12.7147 0.3124 7.6135 0.7474

12 13.9700 0.3026 8.3363 0.7583

Parameter Estimates



Intercept 1 845.0246 113.7941 7.43 <.0001


Q3 1 -62.5540 5.4324 -11.51 <.0001 QUARTER 3 INTERCEPT SHIFT



ENMD1 1 -0.1994 0.0520 -3.84 0.0005 INTERACTION TERM FOR NON MFG EMPLOYMENT BETWEEN 2007Q1 AND 2008Q4

ENMD2 1 0.1778 0.0484 3.67 0.0008 INTERACTION TERM FOR NON MFG EMPLOYMENT BETWEEN 2012Q1 AND 2012Q3

ENMD3 1 0.1970 0.0454 4.34 0.0001 INTERACTION TERM FOR NON MFG EMPLOYMENT BETWEEN 2015Q4 AND 2016Q3

ENMD4 1 -0.0904 0.0523 -1.73 0.0927 INTERACTION TERM FOR NON MFG EMPLOYMENT BETWEEN 2016Q4 AND 2017Q1



Page A-208




Parameter Estimates



D2015Q1 1 45.4576 15.7786 2.88 0.0067 INTERCEPT DUMMY YEAR 2015 QUARTER 1

AR1 1 -0.5106 0.1733 -2.95 0.0057



Page A-209






Page A-210

FULL MODEL FORECAST



1 2724.87 -24.5394 2007 1 2696.333333 -28.5398 -0.010585 -1.49956

2 2625.05 6.6162 2007 2 2631.666667 6.6162 0.002514 0.40430

3 2575.98 4.6829 2007 3 2580.666667 4.6829 0.001815 0.28617

4 2686.36 -21.3605 2007 4 2665 -21.3605 -0.008015 -1.30530

5 2726.02 -12.0158 2008 1 2714 -12.0158 -0.004427 -0.73427

6 2639.96 3.7100 2008 2 2643.666667 3.7100 0.001403 0.22671

7 2580.55 15.7810 2008 3 2596.333333 15.7810 0.006078 0.96435

8 2685.49 36.5072 2008 4 2722 36.5072 0.013412 2.23089

9 2793.00 -9.0042 2009 1 2784 -9.0042 -0.003234 -0.55023

10 2681.23 -11.2284 2009 2 2670 -11.2284 -0.004205 -0.68615

11 2611.10 -11.4354 2009 3 2599.666667 -11.4354 -0.004399 -0.69880

12 2715.58 7.0915 2009 4 2722.666667 7.0915 0.002605 0.43335

13 2776.55 9.7819 2010 1 2786.333333 9.7819 0.003511 0.59776

14 2699.46 -2.4648 2010 2 2697 -2.4648 -0.000914 -0.15062

15 2638.73 -9.0600 2010 3 2629.666667 -9.0600 -0.003445 -0.55364

16 2741.68 -11.3479 2010 4 2730.333333 -11.3479 -0.004156 -0.69345

17 2790.74 19.2622 2011 1 2810 19.2622 0.006855 1.17708

18 2721.41 0.5902 2011 2 2722 0.5902 0.000217 0.03607

19 2666.99 30.3452 2011 3 2697.333333 30.3452 0.011250 1.85434

20 2797.37 1.2977 2011 4 2798.666667 1.2977 0.000464 0.07930

21 2899.18 -11.8417 2012 1 2887.333333 -11.8417 -0.004101 -0.72363

22 2814.15 9.5196 2012 2 2823.666667 9.5196 0.003371 0.58173

23 2767.25 -10.5837 2012 3 2756.666667 -10.5837 -0.003839 -0.64675

24 2822.01 -24.0104 2012 4 2798 -24.0104 -0.008581 -1.46723

25 2867.20 -3.8625 2013 1 2863.333333 -3.8625 -0.001349 -0.23603

26 2791.99 -11.6588 2013 2 2780.333333 -11.6588 -0.004193 -0.71245

27 2736.42 16.9139 2013 3 2753.333333 16.9139 0.006143 1.03358

28 2861.96 19.3739 2013 4 2881.333333 19.3739 0.006724 1.18390

29 2926.64 17.0242 2014 1 2943.666667 17.0242 0.005783 1.04032

30 2850.18 -0.1822 2014 2 2850 -0.1822 -0.000064 -0.01114

31 2789.59 3.0762 2014 3 2792.666667 3.0762 0.001102 0.18798

32 2900.03 25.3001 2014 4 2925.333333 25.3001 0.008649 1.54605

33 3012.95 -2.6129 2015 1 3010.333333 -2.6129 -0.000868 -0.15967

34 2879.79 -5.1264 2015 2 2874.666667 -5.1264 -0.001783 -0.31327

35 2820.00 -4.6658 2015 3 2815.333333 -4.6658 -0.001657 -0.28512



Page A-211

FULL MODEL FORECAST



36 2985.95 -11.9458 2015 4 2974 -11.9458 -0.004017 -0.72999

37 3037.05 14.2850 2016 1 3051.333333 14.2850 0.004682 0.87293

38 2968.13 27.5367 2016 2 2995.666667 27.5367 0.009192 1.68272

39 2923.76 -19.0929 2016 3 2904.666667 -19.0929 -0.006573 -1.16674

40 2929.04 -1.3728 2016 4 2927.666667 -1.3728 -0.000469 -0.08389

41 2981.28 0.3874 2017 1 2981.666667 0.3874 0.000130 0.02368

42 2925.31 -2.3139 2017 2 2923 -2.3139 -0.000792 -0.14140

43 2871.08 -10.0797 2017 3 2861 -10.0797 -0.003523 -0.61596

44 2981.33 -7.9934 2017 4 2973.333333 -7.9934 -0.002688 -0.48846

45 3039.43 1.2368 2018 1 3040.666667 1.2368 0.000407 0.07558

46 2967.00 -12.0005 2018 2 2955 -12.0005 -0.004061 -0.73333



Page A-212

LAWRENCE COMMERCIAL & INDUSTRIAL LOW CUSTOMER COUNT REGRESSION EX POST




LAWRENCE COMMERCIAL & INDUSTRIAL LOW CUSTOMER COUNT



Page A-213


03:35 Monday, October 28, 2019 10


1 2017 3 2861 2902.02 -41.0190 -0.014337

2 2017 4 2973.333333 2996.18 -22.8482 -0.007684

3 2018 1 3040.666667 3040.23 0.4362 0.000143

4 2018 2 2955 2990.03 -35.0339 -0.011856



Page A-214


03:35 Monday, October 28, 2019 11


1 D2015Q1 Parameter Estimate for D2015Q1 45.46 48.97 (7.2%) -3.52


3 ENMD2 Parameter Estimate for ENMD2 0.18 0.16 8.5% 0.01

4 ENMD3 Parameter Estimate for ENMD3 0.20 0.22 ( 12%) -0.03



7 Q1 Parameter Estimate for Q1 88.18 87.64 .62% 0.54

8 Q3 Parameter Estimate for Q3 -62.55 -61.68 1.4% -0.87


10 _A_1 Parameter Estimate for _A_1 -0.51 . . .












22 enm Parameter Estimate for enm 7.02 7.13 (1.6%) -0.11



Page A-215


The CORR Procedure




UPC RPQ1 RPQ2 RPQ4 RPQ1D0 RPQ2D0 RPQ4D0

UPC LAWRENCE COMMERCIAL & INDUSTRIAL LOW USE PER CUSTOMER

1.00000

50

0.88823 <.0001

50

-0.11579 0.4233

50

-0.13420 0.3528

50

0.61786 <.0001

50

-0.08131 0.5746

50

-0.08495 0.5575

50

RPQ1 REAL PRICE * QUARTER 1

0.88823 <.0001

50

1.00000

100

-0.31561 0.0014

100

-0.31686 0.0013

100

0.63078 <.0001

100

-0.15189 0.1314

100

-0.14067 0.1627

100


-0.11579 0.4233

50

-0.31561 0.0014

100

1.00000

100

-0.31681 0.0013

100

-0.15203 0.1311

100

0.62736 <.0001

100

-0.14065 0.1628

100


-0.13420 0.3528

50

-0.31686 0.0013

100

-0.31681 0.0013

100

1.00000

100

-0.15263 0.1295

100

-0.15247 0.1299

100

0.59032 <.0001

100

RPQ1D0 REAL PRICE * QUARTER 1 FROM 2006Q1 TO 2012Q4

0.61786 <.0001

50

0.63078 <.0001

100

-0.15203 0.1311

100

-0.15263 0.1295

100

1.00000

100

-0.07317 0.4694

100

-0.06776 0.5029

100


-0.08131 0.5746

50

-0.15189 0.1314

100

0.62736 <.0001

100

-0.15247 0.1299

100

-0.07317 0.4694

100

1.00000

100

-0.06769 0.5034

100


-0.08495 0.5575

50

-0.14067 0.1627

100

-0.14065 0.1628

100

0.59032 <.0001

100

-0.06776 0.5029

100

-0.06769 0.5034

100

1.00000

100

EDD EFFECTIVE HEATING DEGREE DAYS

0.98387 <.0001

50

0.83443 <.0001

100

-0.16133 0.1088

100

0.01925 0.8492

100

0.34170 0.0005

100

-0.09536 0.3453

100

-0.00863 0.9321

100


-0.00407 0.9776

50

-0.05647 0.5768

100

0.23595 0.0181

100

-0.05668 0.5754

100

-0.02720 0.7882

100

0.37551 0.0001

100

-0.02516 0.8037

100

D2014Q1to2016Q3 INTERCEPT DUMMY FROM 2014Q1 TO 2016Q3

0.01343 0.9263

50

0.00852 0.9330

100

0.00305 0.9760

100

-0.04791 0.6360

100

-0.09515 0.3464

100

-0.09505 0.3469

100

-0.08802 0.3838

100



EDD D2009Q2 D2014Q1to2016Q3

UPC LAWRENCE COMMERCIAL & INDUSTRIAL LOW USE PER CUSTOMER

0.98387 <.0001

50

-0.00407 0.9776

50

0.01343 0.9263

50



Page A-216


The CORR Procedure




EDD D2009Q2 D2014Q1to2016Q3


0.83443 <.0001

100

-0.05647 0.5768

100

0.00852 0.9330

100


-0.16133 0.1088

100

0.23595 0.0181

100

0.00305 0.9760

100


0.01925 0.8492

100

-0.05668 0.5754

100

-0.04791 0.6360

100


0.34170 0.0005

100

-0.02720 0.7882

100

-0.09515 0.3464

100


-0.09536 0.3453

100

0.37551 0.0001

100

-0.09505 0.3469

100


-0.00863 0.9321

100

-0.02516 0.8037

100

-0.08802 0.3838

100

EDD EFFECTIVE HEATING DEGREE DAYS

1.00000

100

-0.03679 0.7163

100

-0.01421 0.8884

100


-0.03679 0.7163

100

1.00000

100

-0.03533 0.7271

100

D2014Q1to2016Q3 INTERCEPT DUMMY FROM 2014Q1 TO 2016Q3

-0.01421 0.8884

100

-0.03533 0.7271

100

1.00000

100



Page A-217

LAWRENCE COMMERCIAL & INDUSTRIAL LLF USE PER CUSTOMER REGRESSION




LAWRENCE COMMERCIAL & INDUSTRIAL LOW USE PER CUSTOMER



Chow 28 10 30 1.82 0.0994



7 0.018895 0.11 0.9144

4 -0.022189 -0.13 0.8999

3 0.085421 0.50 0.6232

1 -0.084296 -0.53 0.6025

5 0.107445 0.68 0.4991

6 -0.145929 -0.91 0.3707

8 0.147690 0.95 0.3488



Error t Value

2 -0.266828 0.154323 -1.73



Page A-218





SSE 461.429894 DFE 39

MSE 11.83154 Root MSE 3.43970

SBC 296.667231 AIC 275.634978

MAE 2.23770439 AICC 282.582346

MAPE 6.05593009 HQC 283.64418



Observations 50



1 0.7857 0.3754 0.6423 0.4229

2 0.8450 0.6554 0.8092 0.6672

3 1.0462 0.7901 1.1718 0.7598

4 2.1258 0.7126 2.4346 0.6564

5 3.2654 0.6591 4.0630 0.5404

6 4.3265 0.6326 5.9367 0.4303

7 4.5983 0.7089 6.1499 0.5224

8 4.9413 0.7638 7.3323 0.5012

9 6.6597 0.6725 7.3323 0.6026

10 7.0225 0.7233 7.3519 0.6919

11 8.9845 0.6233 9.1090 0.6118

12 19.4969 0.0772 11.9240 0.4518



Page A-219




Parameter Estimates



Intercept 1 6.4139 1.5050 4.26 0.0001

RPQ1 1 -1.7462 0.5635 -3.10 0.0036 REAL PRICE * QUARTER 1

RPQ2 1 -0.8260 0.2877 -2.87 0.0066 REAL PRICE * QUARTER 2

RPQ4 1 -2.1122 0.3198 -6.60 <.0001 REAL PRICE * QUARTER 4

RPQ1D0 1 0.8889 0.2118 4.20 0.0002 REAL PRICE * QUARTER 1 FROM 2006Q1 TO 2012Q4



EDD 1 0.0414 0.001828 22.65 <.0001 EFFECTIVE HEATING DEGREE DAYS

D2009Q2 1 10.5417 3.3518 3.15 0.0032 INTERCEPT DUMMY YEAR 2009 QUARTER 2

D2014Q1to2016Q3 1 5.0124 1.8224 2.75 0.0090 INTERCEPT DUMMY FROM 2014Q1 TO 2016Q3

AR2 1 -0.5183 0.1425 -3.64 0.0008



Page A-220






Page A-221

FULL MODEL FORECAST


Obs PRED RES YEAR Q UPC DIFF PCTDIFF SRES

1 130.421 -7.6721 2006 1 121.450 -8.9713 -0.07387 -2.23044

2 56.846 -1.1550 2006 2 55.496 -1.3506 -0.02434 -0.33579

3 6.953 4.9787 2006 3 11.932 4.9787 0.41727 1.44742

4 44.064 4.8451 2006 4 48.910 4.8451 0.09906 1.40859

5 142.013 0.4984 2007 1 142.512 0.4984 0.00350 0.14490

6 63.564 1.1245 2007 2 64.689 1.1245 0.01738 0.32690

7 12.270 1.7491 2007 3 14.019 1.7491 0.12477 0.50849

8 60.375 -0.1114 2007 4 60.264 -0.1114 -0.00185 -0.03240

9 138.543 1.7921 2008 1 140.336 1.7921 0.01277 0.52100

10 60.712 0.6953 2008 2 61.407 0.6953 0.01132 0.20215

11 11.450 1.4242 2008 3 12.874 1.4242 0.11062 0.41404

12 61.535 0.7176 2008 4 62.253 0.7176 0.01153 0.20864

13 150.699 10.8708 2009 1 161.570 10.8708 0.06728 3.16038

14 66.709 -0.0789 2009 2 66.630 -0.0789 -0.00118 -0.02293

15 20.455 -0.7402 2009 3 19.714 -0.7402 -0.03755 -0.21519

16 60.338 -0.1523 2009 4 60.185 -0.1523 -0.00253 -0.04427

17 143.002 0.6152 2010 1 143.617 0.6152 0.00428 0.17886

18 47.050 0.9133 2010 2 47.963 0.9133 0.01904 0.26552

19 11.349 0.7294 2010 3 12.078 0.7294 0.06039 0.21205

20 65.887 -4.4611 2010 4 61.426 -4.4611 -0.07263 -1.29694

21 150.854 2.9844 2011 1 153.839 2.9844 0.01940 0.86762

22 58.978 3.6741 2011 2 62.652 3.6741 0.05864 1.06813

23 12.624 0.6309 2011 3 13.254 0.6309 0.04760 0.18342

24 51.559 -0.6544 2011 4 50.905 -0.6544 -0.01286 -0.19025

25 125.698 -2.5893 2012 1 123.109 -2.5893 -0.02103 -0.75277

26 48.537 -2.4869 2012 2 46.050 -2.4869 -0.05400 -0.72300

27 8.956 3.5399 2012 3 12.496 3.5399 0.28329 1.02914

28 53.535 3.0288 2012 4 56.563 3.0288 0.05355 0.88053

29 132.015 -0.6699 2013 1 131.345 -0.6699 -0.00510 -0.19477

30 57.815 -1.7070 2013 2 56.108 -1.7070 -0.03042 -0.49625

31 11.280 -0.8352 2013 3 10.445 -0.8352 -0.07996 -0.24280

32 61.869 -2.9578 2013 4 58.911 -2.9578 -0.05021 -0.85989

33 152.437 -1.1368 2014 1 151.300 -1.1368 -0.00751 -0.33050

34 61.657 2.2654 2014 2 63.922 2.2654 0.03544 0.65862

35 15.121 -3.8233 2014 3 11.298 -3.8233 -0.33842 -1.11153



Page A-222

FULL MODEL FORECAST


Obs PRED RES YEAR Q UPC DIFF PCTDIFF SRES

36 57.849 1.4755 2014 4 59.325 1.4755 0.02487 0.42895

37 159.212 0.5251 2015 1 159.737 0.5251 0.00329 0.15265

38 62.137 -1.8197 2015 2 60.317 -1.8197 -0.03017 -0.52903

39 13.256 -1.3392 2015 3 11.916 -1.3392 -0.11239 -0.38935

40 51.584 -1.3655 2015 4 50.219 -1.3655 -0.02719 -0.39699

41 126.078 -2.8098 2016 1 123.268 -2.8098 -0.02279 -0.81687

42 60.829 -4.4436 2016 2 56.385 -4.4436 -0.07881 -1.29185

43 11.610 1.2781 2016 3 12.888 1.2781 0.09917 0.37157

44 59.808 -5.2025 2016 4 54.605 -5.2025 -0.09527 -1.51248

45 126.550 -4.0912 2017 1 122.459 -4.0912 -0.03341 -1.18942

46 56.330 1.8904 2017 2 58.221 1.8904 0.03247 0.54959

47 8.534 1.5748 2017 3 10.109 1.5748 0.15579 0.45783

48 51.555 1.0182 2017 4 52.573 1.0182 0.01937 0.29600

49 133.984 4.5208 2018 1 138.505 4.5208 0.03264 1.31431

50 56.263 -0.2222 2018 2 56.041 -0.2222 -0.00397 -0.06461



Page A-223

LAWRENCE COMMERCIAL & INDUSTRIAL LOW USE PER CUSTOMER REGRESSION EX POST




LAWRENCE COMMERCIAL & INDUSTRIAL LOW USE PER CUSTOMER



Page A-224


03:36 Monday, October 28, 2019 10

Obs YEAR Q UPC XPPRED EXDIFF EXPCTDIFF

1 2017 3 10.109 11.757 -1.64786 -0.16301

2 2017 4 52.573 52.258 0.31502 0.00599

3 2018 1 138.505 133.409 5.09649 0.03680

4 2018 2 56.041 55.699 0.34186 0.00610



Page A-225


03:36 Monday, October 28, 2019 11

Obs _NAME_ _LABEL_ XPEST1 EST1 PCTDIFF DIFF

1 D2009Q2 Parameter Estimate for D2009Q2 10.628 10.542 (.81%) -0.0863

2 D2014Q1to2016Q3 Parameter Estimate for D2014Q1to2016Q3 3.480 5.012 44% 1.5321

3 EDD Parameter Estimate for EDD 0.042 0.041 (.94%) -0.0004


5 RPQ1 Parameter Estimate for RPQ1 -2.021 -1.746 ( 14%) 0.2744

6 RPQ1D0 Parameter Estimate for RPQ1D0 1.126 0.889 ( 21%) -0.2368

7 RPQ2 Parameter Estimate for RPQ2 -0.987 -0.826 ( 16%) 0.1613


9 RPQ4 Parameter Estimate for RPQ4 -2.305 -2.112 (8.4%) 0.1927




13 _A_2 Parameter Estimate for _A_2 . -0.518 . .












Page A-226


The CORR Procedure



CUST LagLDevfromNorm rgcp d1 d4

CUST

1.00000

50

-0.66949 <.0001

50

-0.04447 0.7591

50

0.10889 0.4516

50

0.06223 0.6677

50

LagLDevfromNorm Q4-Q3 EDD Deviation from Normal

-0.66949 <.0001

50

1.00000

172

-0.09800 0.2009

172

0.00792 0.9179

172

-0.02376 0.7570

172

rgcp

-0.04447 0.7591

50

-0.09800 0.2009

172

1.00000

172

-0.01745 0.8202

172

0.01736 0.8212

172

d1 Dummy for Q1

0.10889 0.4516

50

0.00792 0.9179

172

-0.01745 0.8202

172

1.00000

172

-0.33333 <.0001

172

d4 Dummy for Q4

0.06223 0.6677

50

-0.02376 0.7570

172

0.01736 0.8212

172

-0.33333 <.0001

172

1.00000

172

d6 Interaction term for pre Q4 2008*LagLDevfromNorm

0.39368 0.0047

50

0.12526 0.1016

172

-0.39391 <.0001

172

0.01454 0.8498

172

-0.04362 0.5699

172

d8 Dummy for data between Q3 2010 and Q2 2013

-0.25430 0.0747

50

-0.17929 0.0186

172

-0.28879 0.0001

172

0.02869 0.7087

172

0.02869 0.7087

172

d10 Interaction term for RGCP between Q3 2010 and Q2 2013

-0.25291 0.0764

50

-0.17861 0.0191

172

-0.28865 0.0001

172

0.02870 0.7086

172

0.02807 0.7147

172

d11 Dummy for data after Q1 2015

0.00914 0.9497

50

-0.04073 0.5958

172

0.68216 <.0001

172

-0.02451 0.7496

172

0.00817 0.9153

172

d12 Interaction term for post Q1 2015 *Lag EDD Deviation from Norm

-0.05863 0.6859

50

-0.00239 0.9752

172

0.67542 <.0001

172

-0.02619 0.7330

172

0.00600 0.9377

172



d6 d8 d10 d11 d12

CUST

0.39368 0.0047

50

-0.25430 0.0747

50

-0.25291 0.0764

50

0.00914 0.9497

50

-0.05863 0.6859

50



Page A-227


The CORR Procedure



d6 d8 d10 d11 d12

LagLDevfromNorm Q4-Q3 EDD Deviation from Normal

0.12526 0.1016

172

-0.17929 0.0186

172

-0.17861 0.0191

172

-0.04073 0.5958

172

-0.00239 0.9752

172

rgcp

-0.39391 <.0001

172

-0.28879 0.0001

172

-0.28865 0.0001

172

0.68216 <.0001

172

0.67542 <.0001

172

d1 Dummy for Q1

0.01454 0.8498

172

0.02869 0.7087

172

0.02870 0.7086

172

-0.02451 0.7496

172

-0.02619 0.7330

172

d4 Dummy for Q4

-0.04362 0.5699

172

0.02869 0.7087

172

0.02807 0.7147

172

0.00817 0.9153

172

0.00600 0.9377

172

d6 Interaction term for pre Q4 2008*LagLDevfromNorm

1.00000

172

-0.06489 0.3977

172

-0.06489 0.3977

172

-0.49890 <.0001

172

-0.49799 <.0001

172

d8 Dummy for data between Q3 2010 and Q2 2013

-0.06489 0.3977

172

1.00000

172

0.99994 <.0001

172

-0.47458 <.0001

172

-0.47371 <.0001

172

d10 Interaction term for RGCP between Q3 2010 and Q2 2013

-0.06489 0.3977

172

0.99994 <.0001

172

1.00000

172

-0.47455 <.0001

172

-0.47368 <.0001

172

d11 Dummy for data after Q1 2015

-0.49890 <.0001

172

-0.47458 <.0001

172

-0.47455 <.0001

172

1.00000

172

0.99817 <.0001

172

d12 Interaction term for post Q1 2015 *Lag EDD Deviation from Norm

-0.49799 <.0001

172

-0.47371 <.0001

172

-0.47368 <.0001

172

0.99817 <.0001

172

1.00000

172



Page A-228

Lawrence C & I HLF Customer Count Full Model




SSE 5008.79938 DFE 40

MSE 125.21998 Root MSE 11.19017

SBC 411.360509 AIC 392.240279

MAE 7.95147383 AICC 397.881305

MAPE 1.08063887 HQC 399.521372




Chow 24 10 30 0.62 0.7831



1 0.6940 0.4048 0.2403 0.6240

2 1.4520 0.4838 0.5416 0.7628

3 8.9901 0.0294 3.0353 0.3862

4 10.7951 0.0290 3.7997 0.4338

5 10.8751 0.0539 3.8370 0.5731

6 10.8832 0.0921 3.8462 0.6975

7 10.9307 0.1417 4.1736 0.7596

8 11.8788 0.1567 4.2109 0.8376

9 12.3764 0.1929 4.4701 0.8778

10 12.4865 0.2538 4.7081 0.9098

11 15.1671 0.1750 6.5404 0.8350

12 15.2321 0.2290 6.9651 0.8599



Page A-229



Parameter Estimates



Intercept 1 802.7628 68.4517 11.73 <.0001

LagLDevfromNorm 1 -279.0813 29.4980 -9.46 <.0001 Q4-Q3 EDD Deviation from Normal

rgcp 1 0.004991 0.001428 3.50 0.0012

d1 1 11.9613 3.8678 3.09 0.0036 Dummy for Q1

d4 1 11.8469 3.9990 2.96 0.0051 Dummy for Q4

d6 1 46.8529 5.7207 8.19 <.0001 Interaction term for pre Q4 2008*LagLDevfromNorm

d8 1 -913.0206 328.9299 -2.78 0.0083 Dummy for data between Q3 2010 and Q2 2013

d10 1 0.0208 0.007716 2.69 0.0103 Interaction term for RGCP between Q3 2010 and Q2 2013

d11 1 184.2980 48.6871 3.79 0.0005 Dummy for data after Q1 2015

d12 1 -188.8230 46.6316 -4.05 0.0002 Interaction term for post Q1 2015 *Lag EDD Deviation from Norm



7 -0.002828 -0.02 0.9872

4 0.006986 0.04 0.9675

5 -0.062793 -0.37 0.7103

2 0.141557 0.87 0.3901

1 -0.131515 -0.83 0.4132

6 0.178932 1.12 0.2696

3 0.168665 1.07 0.2910

8 0.202137 1.29 0.2050



Page A-230





Page A-231

FULL MODEL FORECAST


1 754.432 -7.0982 2006 1 747.3333333 -7.0982 -0.009498 -0.63432

2 743.230 -3.8963 2006 2 739.3333333 -3.8963 -0.005270 -0.34819

3 744.393 0.2732 2006 3 744.6666667 0.2732 0.000367 0.02441

4 779.595 -13.9287 2006 4 765.6666667 -13.9287 -0.018192 -1.24472

5 781.076 -9.0759 2007 1 772 -9.0759 -0.011756 -0.81106

6 770.481 0.8526 2007 2 771.3333333 0.8526 0.001105 0.07619

7 770.557 -0.8904 2007 3 769.6666667 -0.8904 -0.001157 -0.07957

8 777.442 4.5584 2007 4 782 4.5584 0.005829 0.40736

9 776.988 17.0124 2008 1 794 17.0124 0.021426 1.52030

10 764.458 17.5423 2008 2 782 17.5423 0.022433 1.56765

11 764.309 -4.9757 2008 3 759.3333333 -4.9757 -0.006553 -0.44464

12 728.595 11.0721 2008 4 739.6666667 11.0721 0.014969 0.98945

13 728.770 3.5631 2009 1 732.3333333 3.5631 0.004865 0.31841

14 716.870 1.1298 2009 2 718 1.1298 0.001574 0.10097

15 718.548 -9.5480 2009 3 709 -9.5480 -0.013467 -0.85325

16 718.458 5.5422 2009 4 724 5.5422 0.007655 0.49528

17 721.058 -0.0584 2010 1 721 -0.0584 -0.000081 -0.00522

18 711.583 4.0835 2010 2 715.6666667 4.0835 0.005706 0.36492

19 713.466 -1.7998 2010 3 711.6666667 -1.7998 -0.002529 -0.16083

20 710.030 7.9701 2010 4 718 7.9701 0.011100 0.71224

21 718.309 2.6912 2011 1 721 2.6912 0.003733 0.24050

22 714.512 -5.1786 2011 2 709.3333333 -5.1786 -0.007301 -0.46278

23 720.181 -16.5145 2011 3 703.6666667 -16.5145 -0.023469 -1.47580

24 707.711 -1.0443 2011 4 706.6666667 -1.0443 -0.001478 -0.09333

25 712.247 -3.5803 2012 1 708.6666667 -3.5803 -0.005052 -0.31995

26 704.707 -12.0404 2012 2 692.6666667 -12.0404 -0.017383 -1.07598

27 705.750 11.9166 2012 3 717.6666667 11.9166 0.016605 1.06491

28 771.209 5.1242 2012 4 776.3333333 5.1242 0.006601 0.45792

29 770.677 10.6560 2013 1 781.3333333 10.6560 0.013638 0.95226

30 777.490 -4.4902 2013 2 773 -4.4902 -0.005809 -0.40126

31 777.744 -17.4105 2013 3 760.3333333 -17.4105 -0.022898 -1.55587

32 748.861 -1.5278 2013 4 747.3333333 -1.5278 -0.002044 -0.13653

33 749.419 7.9148 2014 1 757.3333333 7.9148 0.010451 0.70730

34 737.900 14.4331 2014 2 752.3333333 14.4331 0.019184 1.28980

35 739.190 -0.8570 2014 3 738.3333333 -0.8570 -0.001161 -0.07659



Page A-232

FULL MODEL FORECAST


36 722.796 -0.7963 2014 4 722 -0.7963 -0.001103 -0.07116

37 724.624 -11.6245 2015 1 713 -11.6245 -0.016304 -1.03881

38 688.172 18.4943 2015 2 706.6666667 18.4943 0.026171 1.65273

39 688.772 1.8946 2015 3 690.6666667 1.8946 0.002743 0.16931

40 701.920 -22.9202 2015 4 679 -22.9202 -0.033756 -2.04824

41 702.077 -17.0774 2016 1 685 -17.0774 -0.024931 -1.52611

42 690.159 -11.8256 2016 2 678.3333333 -11.8256 -0.017433 -1.05678

43 690.991 19.0085 2016 3 710 19.0085 0.026773 1.69868

44 797.196 -6.5291 2016 4 790.6666667 -6.5291 -0.008258 -0.58347

45 798.538 -3.2044 2017 1 795.3333333 -3.2044 -0.004029 -0.28636

46 787.804 -0.1372 2017 2 787.6666667 -0.1372 -0.000174 -0.01226

47 789.424 -10.7575 2017 3 778.6666667 -10.7575 -0.013815 -0.96133

48 768.854 12.4794 2017 4 781.3333333 12.4794 0.015972 1.11521

49 770.785 9.8816 2018 1 780.6666667 9.8816 0.012658 0.88306

50 760.640 10.6930 2018 2 771.3333333 10.6930 0.013863 0.95557



Page A-233

Lawrence C & I HLF Customer Count Ex Post Model





Page A-234


Obs EXPRED YEAR Q CUST EXDIFF EXPCTDIFF

1 782.865 2017 2 787.6666667 4.8018 0.006096

2 784.209 2017 3 778.6666667 -5.5425 -0.007118

3 763.272 2017 4 781.3333333 18.0612 0.023116

4 765.225 2018 1 780.6666667 15.4415 0.019780

5 755.377 2018 2 771.3333333 15.9560 0.020686



Page A-235





3 LagLDevfromNorm Parameter Estimate for LagLDevfromNorm -279.08 -279.68 (.21%) 0.600













16 d10 Parameter Estimate for d10 0.02 0.02 (3.1%) -0.001


18 d12 Parameter Estimate for d12 -188.82 -176.19 7.2% -12.632



21 d8 Parameter Estimate for d8 -913.02 -940.87 (3.0%) 27.854

22 rgcp Parameter Estimate for rgcp 0.00 0.00 21% 0.001



Page A-236


The CORR Procedure



CIHLFUPC LMEDDQ1 LMEDDQ2 LMEDDQ4 ACIHLP d6 d7 d8

CIHLFUPC

1.00000

38

0.74223 <.0001

38

0.06365 0.7042

38

-0.25196 0.1270

38

0.27965 0.0891

38

0.29656 0.0706

38

-0.34520 0.0338

38

-0.03662 0.8272

38

LMEDDQ1

0.74223 <.0001

38

1.00000

88

-0.33116 0.0016

88

-0.33145 0.0016

88

0.07147 0.5081

88

0.18500 0.0844

88

-0.02099 0.8461

88

-0.06171 0.5679

88

LMEDDQ2

0.06365 0.7042

38

-0.33116 0.0016

88

1.00000

88

-0.33129 0.0016

88

0.00972 0.9284

88

-0.06168 0.5681

88

0.00657 0.9515

88

0.17626 0.1004

88

LMEDDQ4

-0.25196 0.1270

38

-0.33145 0.0016

88

-0.33129 0.0016

88

1.00000

88

-0.04424 0.6824

88

-0.06174 0.5677

88

0.01524 0.8880

88

-0.06174 0.5677

88

ACIHLP

0.27965 0.0891

38

0.07147 0.5081

88

0.00972 0.9284

88

-0.04424 0.6824

88

1.00000

128

0.13601 0.1258

128

-0.85148 <.0001

128

0.34832 <.0001

128


0.29656 0.0706

38

0.18500 0.0844

88

-0.06168 0.5681

88

-0.06174 0.5677

88

0.13601 0.1258

128

1.00000

160

-0.17219 0.0295

160

-0.00629 0.9371

160

d7 post q4 2015 dummy

-0.34520 0.0338

38

-0.02099 0.8461

88

0.00657 0.9515

88

0.01524 0.8880

88

-0.85148 <.0001

128

-0.17219 0.0295

160

1.00000

160

-0.17219 0.0295

160


-0.03662 0.8272

38

-0.06171 0.5679

88

0.17626 0.1004

88

-0.06174 0.5677

88

0.34832 <.0001

128

-0.00629 0.9371

160

-0.17219 0.0295

160

1.00000

160



Page A-237

Lawrence C&I HLF UPC Full Model





Chow 18 8 22 0.32 0.9476



6 0.020663 0.09 0.9291

5 0.038760 0.19 0.8498

2 0.079259 0.37 0.7123

3 -0.082957 -0.43 0.6675

7 0.106384 0.57 0.5718

8 0.276804 1.70 0.1006

4 0.238767 1.55 0.1326



Error t Value

1 -0.536134 0.156752 -3.42



Page A-238




SSE 2767.18837 DFE 29

MSE 95.42029 Root MSE 9.76833

SBC 305.714637 AIC 290.976362

MAE 6.99601828 AICC 297.404933

MAPE 3.76100065 HQC 296.220127



Observations 38



1 0.3162 0.5739 0.0278 0.8675

2 1.6981 0.4278 0.3321 0.8470

3 1.7858 0.6180 0.3801 0.9443

4 7.7433 0.1014 2.0817 0.7207

5 9.6522 0.0857 2.9042 0.7148

6 10.5706 0.1026 3.9121 0.6886

7 11.7292 0.1098 4.9779 0.6627

8 15.0395 0.0584 7.6127 0.4722

9 24.6038 0.0034 8.2812 0.5061

10 25.2038 0.0050 8.3553 0.5942

11 26.0896 0.0063 9.0707 0.6154

12 28.5754 0.0046 9.2859 0.6783



Page A-239



Parameter Estimates



Intercept 1 206.8687 40.5292 5.10 <.0001

LMEDDQ1 1 0.0294 0.000976 30.12 <.0001

LMEDDQ2 1 0.0404 0.002182 18.53 <.0001

LMEDDQ4 1 0.0137 0.001635 8.40 <.0001

ACIHLP 1 -6.4937 2.6264 -2.47 0.0195

d6 1 21.5058 7.6321 2.82 0.0086 Dummy for Q1 2010

d7 1 -25.2274 11.7369 -2.15 0.0401 post q4 2015 dummy

d8 1 18.6581 7.5297 2.48 0.0193 Dummy for Q2 2009

AR1 1 -0.9426 0.0680 -13.85 <.0001



Page A-240





Page A-241

FULL MODEL FORECAST

Obs Pupc Rupc YEAR Q CIHLFUPC DIFF PCTDIFF SRES

1 215.328 -1.7956 2009 1 209.9526627 -5.3755 -0.02560 -0.18382

2 178.157 11.3520 2009 2 189.5092851 11.3520 0.05990 1.16212

3 128.973 12.0471 2009 3 141.0202163 12.0471 0.08543 1.23328

4 172.410 -9.1817 2009 4 163.2279006 -9.1817 -0.05625 -0.93994

5 267.475 12.9711 2010 1 280.4466019 12.9711 0.04625 1.32787

6 201.757 13.7654 2010 2 215.5221239 13.7654 0.06387 1.40918

7 166.814 6.5848 2010 3 173.3990632 6.5848 0.03797 0.67410

8 194.681 10.3451 2010 4 205.0259981 10.3451 0.05046 1.05904

9 286.731 3.5381 2011 1 290.2686084 3.5381 0.01219 0.36220

10 233.880 -0.6922 2011 2 233.1879699 -0.6922 -0.00297 -0.07086

11 174.107 -0.6553 2011 3 173.4514448 -0.6553 -0.00378 -0.06709

12 194.262 1.8044 2011 4 196.0660377 1.8044 0.00920 0.18472

13 267.644 1.4159 2012 1 269.0597366 1.4159 0.00526 0.14495

14 224.342 -7.7465 2012 2 216.5957652 -7.7465 -0.03576 -0.79302

15 171.649 -20.7073 2012 3 150.9419415 -20.7073 -0.13719 -2.11984

16 177.075 -3.1394 2012 4 173.9355947 -3.1394 -0.01805 -0.32138

17 252.661 10.1653 2013 1 262.8259386 10.1653 0.03868 1.04064

18 215.468 2.4638 2013 2 217.9318672 2.4638 0.01131 0.25222

19 155.118 4.9031 2013 3 160.020605 4.9031 0.03064 0.50193

20 184.366 11.4942 2013 4 195.8599465 11.4942 0.05869 1.17668

21 278.796 6.2707 2014 1 285.0669014 6.2707 0.02200 0.64194

22 223.419 2.9697 2014 2 226.389012 2.9697 0.01312 0.30401

23 163.736 3.1830 2014 3 166.9191874 3.1830 0.01907 0.32585

24 189.309 8.8246 2014 4 198.1334257 8.8246 0.04454 0.90339

25 294.619 -7.6270 2015 1 286.9920524 -7.6270 -0.02658 -0.78079

26 229.107 -0.4386 2015 2 228.6683962 -0.4386 -0.00192 -0.04490

27 171.358 -9.6310 2015 3 161.7273166 -9.6310 -0.05955 -0.98594

28 189.155 -15.9804 2015 4 173.1742759 -15.9804 -0.09228 -1.63594

29 230.264 2.3195 2016 1 232.583455 2.3195 0.00997 0.23746

30 199.090 -2.1902 2016 2 196.8997543 -2.1902 -0.01112 -0.22422

31 141.207 -4.8162 2016 3 136.3910798 -4.8162 -0.03531 -0.49304

32 160.919 -4.3629 2016 4 156.5564924 -4.3629 -0.02787 -0.44664

33 228.435 -8.6367 2017 1 219.7984074 -8.6367 -0.03929 -0.88415

34 183.387 -13.2665 2017 2 170.1201862 -13.2665 -0.07798 -1.35811

35 111.258 6.0592 2017 3 117.3172089 6.0592 0.05165 0.62029



Page A-242

FULL MODEL FORECAST


36 140.476 4.7586 2017 4 145.2350683 4.7586 0.03276 0.48714

37 222.331 -13.9812 2018 1 208.3501281 -13.9812 -0.06710 -1.43128

38 156.379 -3.7645 2018 2 152.6149525 -3.7645 -0.02467 -0.38538



Page A-243

Lawrence C&I HLF UPC Ex Post Model





Page A-244


Obs YEAR Q CIHLFUPC EXDIFF EXPCTDIFF

1 2017 3 117.3172089 7.3145 0.062348

2 2017 4 145.2350683 12.2009 0.084008

3 2018 1 208.3501281 -4.9421 -0.023720

4 2018 2 152.6149525 -10.0880 -0.066101



Page A-245



1 ACIHLP Parameter Estimate for ACIHLP -6.49 -5.46 19% -1.030



4 LMEDDQ1 Parameter Estimate for LMEDDQ1 0.03 0.03 (.78%) -0.000

5 LMEDDQ2 Parameter Estimate for LMEDDQ2 0.04 0.04 (2.1%) -0.001

6 LMEDDQ4 Parameter Estimate for LMEDDQ4 0.01 0.01 2.8% 0.000

7 _A_1 Parameter Estimate for _A_1 -0.94 -0.94 .42% -0.004
















Page A-246


The CORR Procedure




DTH LMEDD

DTH 1.00000

8

0.94310 0.0004

8

LMEDD 0.94310 0.0004

8

1.00000

58



Page A-247

Lawrence Capacity Exempt Volumes Full Model





SSE 770920710 DFE 6

MSE 128486785 Root MSE 11335

SBC 173.931136 AIC 173.772253

MAE 8629.75317 AICC 176.172253

MAPE 13.3622228 HQC 172.70065




Chow 6 2 4 0.80 0.5085



1 0.1167 0.7326 0.0000 0.9955

2 1.2790 0.5276 1.8404 0.3984

3 4.2228 0.2384 2.5899 0.4593

4 7.5316 0.1103 3.7038 0.4476

5 13.8110 0.0169 4.9333 0.4241

6 18.8547 0.0044 6.6631 0.3531

7 19.1894 0.0076 8.0000 0.3326

8 19.1894 0.0139 . .

9 19.1894 0.0236 . .

10 19.1894 0.0379 . .

11 19.1894 0.0578 . .

12 19.1894 0.0841 . .



Page A-248




Parameter Estimates



Intercept 1 37805 6901 5.48 0.0015

LMEDD 1 23.2027 3.3398 6.95 0.0004



8 -0.059850 -0.06 0.9619

6 -0.056496 -0.05 0.9688

3 -0.104122 -0.09 0.9438

7 0.137798 0.16 0.8960

4 -0.220960 -0.36 0.7517

5 0.284765 0.61 0.5860

2 0.350974 0.75 0.4952

1 -0.412548 -1.01 0.3577



Page A-249






Page A-250

FULL MODEL FORECAST



1 39067.55 11151.25 2016 3 50218.8 11151.25 0.22205 0.98377

2 79403.38 14833.08 2016 4 94236.46667 14833.08 0.15740 1.30859

3 114734.82 7233.78 2017 1 121968.6 7233.78 0.05931 0.63817

4 72642.95 -8654.55 2017 2 63988.4 -8654.55 -0.13525 -0.76351

5 40300.45 -5508.25 2017 3 34792.2 -5508.25 -0.15832 -0.48594

6 75779.54 1300.89 2017 4 77080.43333 1300.89 0.01688 0.11477

7 121021.70 -4696.30 2018 1 116325.4 -4696.30 -0.04037 -0.41431

8 71709.57 -15659.90 2018 2 56049.66667 -15659.90 -0.27939 -1.38153



Page A-251

Lawrence Capacity Exempt Volumes Ex Post Model






Page A-252




1 2017 3 34792.2 47236.40 -12444.20 -0.35767

2 2017 4 77080.43333 81935.44 -4855.01 -0.06299

3 2018 1 116325.4 126182.91 -9857.51 -0.08474

4 2018 2 56049.66667 77954.95 -21905.29 -0.39082



Page A-253






3 LMEDD Parameter Estimate for LMEDD 23.20 22.69 2.2% 0.51













Page A-254


The CORR Procedure




CUST POPQ POPQxbefore2013 before2013 Q1 Q2 Q4

CUST 1.00000

56

0.96719 <.0001

56

-0.89615 <.0001

56

-0.89911 <.0001

56

0.04274 0.7545

56

-0.02438 0.8584

56

0.06382 0.6403

56

POPQ 0.96719 <.0001

56

1.00000

104

-0.84455 <.0001

104

-0.84638 <.0001

104

-0.02799 0.7779

104

-0.00929 0.9254

104

0.02792 0.7784

104

POPQxbefore2013 -0.89615 <.0001

56

-0.84455 <.0001

104

1.00000

104

0.99997 <.0001

104

-0.00034 0.9973

104

-0.00012 0.9991

104

0.00034 0.9972

104

before2013 -0.89911 <.0001

56

-0.84638 <.0001

104

0.99997 <.0001

104

1.00000

104

0.00000 1.0000

104

0.00000 1.0000

104

0.00000 1.0000

104

Q1 0.04274 0.7545

56

-0.02799 0.7779

104

-0.00034 0.9973

104

0.00000 1.0000

104

1.00000

104

-0.33333 0.0005

104

-0.33333 0.0005

104

Q2 -0.02438 0.8584

56

-0.00929 0.9254

104

-0.00012 0.9991

104

0.00000 1.0000

104

-0.33333 0.0005

104

1.00000

104

-0.33333 0.0005

104

Q4 0.06382 0.6403

56

0.02792 0.7784

104

0.00034 0.9972

104

0.00000 1.0000

104

-0.33333 0.0005

104

-0.33333 0.0005

104

1.00000

104

POPQxY2016Q3andAFTER 0.64146 <.0001

56

0.84411 <.0001

104

-0.73412 <.0001

104

-0.73414 <.0001

104

-0.01136 0.9089

104

-0.01114 0.9106

104

0.03343 0.7362

104

Y2016Q3andAFTER 0.64141 <.0001

56

0.84124 <.0001

104

-0.73415 <.0001

104

-0.73417 <.0001

104

-0.01115 0.9105

104

-0.01115 0.9105

104

0.03346 0.7359

104



POPQxY2016Q3andAFTER Y2016Q3andAFTER

CUST 0.64146 <.0001

56

0.64141 <.0001

56

POPQ 0.84411 <.0001

104

0.84124 <.0001

104



Page A-255


The CORR Procedure




POPQxY2016Q3andAFTER Y2016Q3andAFTER

POPQxbefore2013 -0.73412 <.0001

104

-0.73415 <.0001

104

before2013 -0.73414 <.0001

104

-0.73417 <.0001

104

Q1 -0.01136 0.9089

104

-0.01115 0.9105

104

Q2 -0.01114 0.9106

104

-0.01115 0.9105

104

Q4 0.03343 0.7362

104

0.03346 0.7359

104

POPQxY2016Q3andAFTER 1.00000

104

0.99996 <.0001

104

Y2016Q3andAFTER 0.99996 <.0001

104

1.00000

104



Page A-256

Springfield Residential Heating Customer Count Full Model






Chow 21 9 38 0.93 0.5134



Error t Value

1 -1.260940 0.124216 -10.15

2 0.552875 0.124216 4.45


SSE 1040715.58 DFE 45

MSE 23127 Root MSE 152.07568

SBC 755.935718 AIC 733.65685

MAE 98.1071678 AICC 739.65685

MAPE 0.12281644 HQC 742.29432



Observations 56



Page A-257






1 2.8833 0.0895 1.4413 0.2299

2 2.9102 0.2334 1.5026 0.4718

3 4.2233 0.2383 2.0922 0.5535

4 4.4298 0.3510 2.2669 0.6868

5 8.0316 0.1545 3.9292 0.5596

6 11.1450 0.0840 6.3419 0.3860

7 14.1004 0.0494 7.0345 0.4253

8 15.0079 0.0590 7.8061 0.4526

9 15.4006 0.0805 8.0548 0.5286

10 18.0501 0.0541 9.2654 0.5071

11 19.5439 0.0520 9.5404 0.5721

12 22.4556 0.0327 13.7589 0.3164

Parameter Estimates



Intercept 1 -1116096 72936 -15.30 <.0001

POPQ 1 1904 115.8189 16.44 <.0001

POPQxbefore2013 1 -1331 132.7196 -10.03 <.0001

before2013 1 835968 83405 10.02 <.0001

Q1 1 1613 41.7386 38.64 <.0001

Q2 1 765.3597 31.9096 23.99 <.0001

Q4 1 974.0213 33.1073 29.42 <.0001

POPQxY2016Q3andAFTER 1 -881.5436 257.2469 -3.43 0.0013

Y2016Q3andAFTER 1 556309 162386 3.43 0.0013

AR1 1 -1.3767 0.1168 -11.78 <.0001

AR2 1 0.6212 0.1167 5.32 <.0001



Page A-258






Page A-259

FULL MODEL FORECAST



1 74550.07 -84.321 2005 1 74346.33333 -203.737 -.002740381 -0.55447

2 73632.79 -53.121 2005 2 73565 -67.786 -.000921447 -0.34931

3 72961.51 11.159 2005 3 72972.66667 11.159 0.000152925 0.07338

4 74160.74 15.260 2005 4 74176 15.260 0.000205729 0.10035

5 75034.15 -20.484 2006 1 75013.66667 -20.484 -.000273066 -0.13469

6 74351.84 82.161 2006 2 74434 82.161 0.001103804 0.54026

7 73853.59 -302.588 2006 3 73551 -302.588 -.004113988 -1.98972

8 74537.29 116.710 2006 4 74654 116.710 0.001563340 0.76744

9 75451.72 228.278 2007 1 75680 228.278 0.003016356 1.50108

10 75090.36 -139.031 2007 2 74951.33333 -139.031 -.001854950 -0.91422

11 74321.16 -134.164 2007 3 74187 -134.164 -.001808458 -0.88222

12 75254.82 87.843 2007 4 75342.66667 87.843 0.001165918 0.57763

13 76175.92 -47.251 2008 1 76128.66667 -47.251 -.000620679 -0.31071

14 75463.29 1.039 2008 2 75464.33333 1.039 0.000013771 0.00683

15 74882.24 -18.239 2008 3 74864 -18.239 -.000243624 -0.11993

16 76014.83 331.833 2008 4 76346.66667 331.833 0.004346392 2.18202

17 77293.82 -11.155 2009 1 77282.66667 -11.155 -.000144346 -0.07335

18 76577.57 52.434 2009 2 76630 52.434 0.000684255 0.34479

19 76080.40 207.937 2009 3 76288.33333 207.937 0.002725671 1.36733

20 77571.74 -15.075 2009 4 77556.66667 -15.075 -.000194372 -0.09913

21 78402.68 32.320 2010 1 78435 32.320 0.000412055 0.21252

22 77786.70 -7.700 2010 2 77779 -7.700 -.000098995 -0.05063

23 77243.90 102.766 2010 3 77346.66667 102.766 0.001328643 0.67576

24 78641.60 -196.933 2010 4 78444.66667 -196.933 -.002510471 -1.29497

25 79341.05 -163.713 2011 1 79177.33333 -163.713 -.002067670 -1.07652

26 78641.03 0.636 2011 2 78641.66667 0.636 0.000008084 0.00418

27 78187.27 -257.270 2011 3 77930 -257.270 -.003301300 -1.69173

28 79121.33 -58.997 2011 4 79062.33333 -58.997 -.000746212 -0.38795

29 80031.29 119.047 2012 1 80150.33333 119.047 0.001485291 0.78281

30 79741.14 51.855 2012 2 79793 51.855 0.000649871 0.34098

31 79440.81 286.853 2012 3 79727.66667 286.853 0.003597912 1.88625

32 81118.29 -25.961 2012 4 81092.33333 -25.961 -.000320146 -0.17071

33 82096.17 -22.172 2013 1 82074 -22.172 -.000270149 -0.14580

34 81738.96 -90.294 2013 2 81648.66667 -90.294 -.001105881 -0.59374

35 81396.29 4.046 2013 3 81400.33333 4.046 0.000049711 0.02661



Page A-260

FULL MODEL FORECAST



36 83001.67 -180.998 2013 4 82820.66667 -180.998 -.002185425 -1.19019

37 84102.38 58.285 2014 1 84160.66667 58.285 0.000692548 0.38327

38 84135.61 -397.276 2014 2 83738.33333 -397.276 -.004744257 -2.61236

39 83280.66 118.673 2014 3 83399.33333 118.673 0.001422948 0.78035

40 84710.45 34.555 2014 4 84745 34.555 0.000407748 0.22722

41 85766.48 -55.483 2015 1 85711 -55.483 -.000647330 -0.36484

42 85177.75 -73.745 2015 2 85104 -73.745 -.000866534 -0.48493

43 84525.76 -47.431 2015 3 84478.33333 -47.431 -.000561457 -0.31189

44 85580.96 100.378 2015 4 85681.33333 100.378 0.001171526 0.66005

45 86482.52 63.817 2016 1 86546.33333 63.817 0.000737369 0.41964

46 85809.52 241.481 2016 2 86051 241.481 0.002806250 1.58790

47 85700.28 39.384 2016 3 85739.66667 39.384 0.000459342 0.25898

48 86929.79 -63.119 2016 4 86866.66667 -63.119 -.000726618 -0.41505

49 87574.83 -58.832 2017 1 87516 -58.832 -.000672240 -0.38686

50 86733.16 290.172 2017 2 87023.33333 290.172 0.003334412 1.90807

51 86562.20 -25.196 2017 3 86537 -25.196 -.000291154 -0.16568

52 87770.87 -110.870 2017 4 87660 -110.870 -.001264768 -0.72904

53 88509.01 -70.010 2018 1 88439 -70.010 -.000791621 -0.46036

54 87844.70 37.962 2018 2 87882.66667 37.962 0.000431964 0.24963

55 87396.98 38.689 2018 3 87435.66667 38.689 0.000442489 0.25441

56 88703.00 7.000 2018 4 88710 7.000 0.000078912 0.04603



Page A-261

Springfield Residential Heating Customer Count Ex Post forecast






Page A-262



Obs YEAR Q CUST XPRED EXDIFF EXPCTDIFF

1 2018 1 88439 88493.74 -54.744 -.000618999

2 2018 2 87882.66667 87887.65 -4.985 -.000056723

3 2018 3 87435.66667 87337.54 98.125 0.001122258

4 2018 4 88710 88530.00 180.002 0.002029104



Page A-263


03:42 Monday, October 28, 2019 10



2 Intercept Intercept Parameter -1116095.99 -1120529.40 (.40%) 4433.41

3 POPQ Parameter Estimate for POPQ 1903.67 1910.72 (.37%) -7.05

4 POPQxY2016Q3andAFTER Parameter Estimate for POPQxY2016Q3andAFTER

-881.54 -955.78 (7.8%) 74.23

5 POPQxbefore2013 Parameter Estimate for POPQxbefore2013 -1330.92 -1337.47 (.49%) 6.55




9 Y2016Q3andAFTER Parameter Estimate for Y2016Q3andAFTER 556308.59 603167.68 (7.8%) -46859.09

10 _A_1 Parameter Estimate for _A_1 -1.38 -1.38 .07% -0.00




14 _SSE_ Sum of Squares Error 1040715.58 1031310.72 .91% 9404.86

15 before2013 Parameter Estimate for before2013 835968.06 840092.37 (.49%) -4124.31



Page A-264


The CORR Procedure




RHUPC SMEDDxQ1 SMEDDxQ2 SMEDDxQ4 ARHHPxQ1

RHUPC 1.00000

68

0.91443 <.0001

68

-0.20628 0.0915

68

-0.03433 0.7810

68

0.89790 <.0001

68

SMEDDxQ1 0.91443 <.0001

68

1.00000

116

-0.33056 0.0003

116

-0.33113 0.0003

116

0.98744 <.0001

116

SMEDDxQ2 -0.20628 0.0915

68

-0.33056 0.0003

116

1.00000

116

-0.33047 0.0003

116

-0.32745 0.0003

116

SMEDDxQ4 -0.03433 0.7810

68

-0.33113 0.0003

116

-0.33047 0.0003

116

1.00000

116

-0.32801 0.0003

116

ARHHPxQ1 0.89790 <.0001

68

0.98744 <.0001

116

-0.32745 0.0003

116

-0.32801 0.0003

116

1.00000

116

ARHHPxQ2 -0.20940 0.0866

68

-0.32805 0.0003

116

0.98558 <.0001

116

-0.32795 0.0003

116

-0.32496 0.0004

116

ARHHPxQ4 -0.04120 0.7387

68

-0.32819 0.0003

116

-0.32753 0.0003

116

0.98181 <.0001

116

-0.32510 0.0004

116

SMEDDxy2008Q2andbefore 0.52156 <.0001

68

0.29553 0.0013

116

-0.03806 0.6850

116

-0.00619 0.9474

116

0.36267 <.0001

116

year2008andafter -0.08290 0.5015

68

-0.00336 0.9714

116

-0.02170 0.8172

116

-0.00198 0.9832

116

-0.04304 0.6464

116

year2009Q1 0.20505 0.0935

68

0.17504 0.0602

116

-0.05356 0.5680

116

-0.05365 0.5673

116

0.20966 0.0239

116

between2015Q3_and2016Q1 -0.05688 0.6450

68

-0.01229 0.8958

116

0.00068 0.9942

116

-0.02023 0.8294

116

-0.02567 0.7845

116



Page A-265


The CORR Procedure




ARHHPxQ2 ARHHPxQ4 SMEDDxy2008Q2andbefore

RHUPC -0.20940 0.0866

68

-0.04120 0.7387

68

0.52156 <.0001

68

SMEDDxQ1 -0.32805 0.0003

116

-0.32819 0.0003

116

0.29553 0.0013

116

SMEDDxQ2 0.98558 <.0001

116

-0.32753 0.0003

116

-0.03806 0.6850

116

SMEDDxQ4 -0.32795 0.0003

116

0.98181 <.0001

116

-0.00619 0.9474

116

ARHHPxQ1 -0.32496 0.0004

116

-0.32510 0.0004

116

0.36267 <.0001

116

ARHHPxQ2 1.00000

116

-0.32504 0.0004

116

-0.02184 0.8160

116

ARHHPxQ4 -0.32504 0.0004

116

1.00000

116

0.02261 0.8096

116

SMEDDxy2008Q2andbefore -0.02184 0.8160

116

0.02261 0.8096

116

1.00000

116

year2008andafter -0.04364 0.6418

116

-0.04560 0.6269

116

-0.69975 <.0001

116

year2009Q1 -0.05315 0.5709

116

-0.05318 0.5708

116

-0.03835 0.6827

116

between2015Q3_and2016Q1 -0.02949 0.7533

116

-0.00734 0.9377

116

-0.07773 0.4069

116



year2008andafter year2009Q1 between2015Q3_and2016Q1

RHUPC -0.08290 0.5015

68

0.20505 0.0935

68

-0.05688 0.6450

68



Page A-266


The CORR Procedure




year2008andafter year2009Q1 between2015Q3_and2016Q1

SMEDDxQ1 -0.00336 0.9714

116

0.17504 0.0602

116

-0.01229 0.8958

116

SMEDDxQ2 -0.02170 0.8172

116

-0.05356 0.5680

116

0.00068 0.9942

116

SMEDDxQ4 -0.00198 0.9832

116

-0.05365 0.5673

116

-0.02023 0.8294

116

ARHHPxQ1 -0.04304 0.6464

116

0.20966 0.0239

116

-0.02567 0.7845

116

ARHHPxQ2 -0.04364 0.6418

116

-0.05315 0.5709

116

-0.02949 0.7533

116

ARHHPxQ4 -0.04560 0.6269

116

-0.05318 0.5708

116

-0.00734 0.9377

116

SMEDDxy2008Q2andbefore -0.69975 <.0001

116

-0.03835 0.6827

116

-0.07773 0.4069

116

year2008andafter 1.00000

116

0.04763 0.6117

116

0.09652 0.3027

116

year2009Q1 0.04763 0.6117

116

1.00000

116

-0.01762 0.8511

116

between2015Q3_and2016Q1 0.09652 0.3027

116

-0.01762 0.8511

116

1.00000

116



Page A-267

Springfield Residential Heat UPC Full Model






Chow 24 11 46 2.05 0.0446



3 0.003091 0.02 0.9837

4 0.016693 0.11 0.9094

5 -0.014760 -0.11 0.9158

6 0.017328 0.13 0.8994

7 0.075875 0.58 0.5639

8 0.184731 1.55 0.1270

2 -0.230590 -1.76 0.0844




Error t Value

1 -0.417316 0.121438 -3.44



Page A-268





SSE 2.17784616 DFE 56

MSE 0.03889 Root MSE 0.19721

SBC 9.86631726 AIC -16.767775

MAE 0.14913335 AICC -11.095048

MAPE 2.75583428 HQC -6.2145321

Log Likelihood 20.3838876 Transformed Regression R-Square 0.9991


Observations 68



1 3.1619 0.0754 1.5889 0.2075

2 3.1912 0.2028 1.5928 0.4510

3 5.5752 0.1342 2.4195 0.4900

4 5.5757 0.2332 2.4403 0.6554

5 8.0960 0.1510 3.3101 0.6523

6 12.7416 0.0473 5.4080 0.4926

7 13.6609 0.0576 7.3184 0.3965

8 18.2201 0.0196 10.3404 0.2419

9 26.0759 0.0020 12.6714 0.1780

10 26.0888 0.0036 13.0040 0.2234

11 26.5059 0.0055 13.0170 0.2922

12 27.1686 0.0073 13.0314 0.3668



Page A-269




Parameter Estimates



Intercept 1 2.2466 0.0874 25.71 <.0001

SMEDDxQ1 1 0.004520 0.0000937 48.22 <.0001

SMEDDxQ2 1 0.004172 0.000224 18.65 <.0001

SMEDDxQ4 1 0.003694 0.000146 25.27 <.0001

ARHHPxQ1 1 -0.1319 0.0228 -5.78 <.0001

ARHHPxQ2 1 -0.0735 0.0170 -4.31 <.0001

ARHHPxQ4 1 -0.0632 0.0161 -3.93 0.0002

SMEDDxy2008Q2andbefore 1 0.000371 0.0000402 9.23 <.0001

year2008andafter 1 -0.3117 0.1022 -3.05 0.0035

year2009Q1 1 0.5003 0.2034 2.46 0.0170

between2015Q3_and2016Q1 1 -0.3704 0.1468 -2.52 0.0145

AR1 1 -0.4754 0.1239 -3.84 0.0003



Page A-270






Page A-271

FULL MODEL FORECAST



1 14.7200 0.04517 2002 1 14.77132113 0.05134 0.00348 0.22904

2 6.8055 0.13529 2002 2 6.940788026 0.13529 0.01949 0.68603

3 2.3431 0.01160 2002 3 2.354673944 0.01160 0.00492 0.05880

4 9.4484 0.25054 2002 4 9.698900002 0.25054 0.02583 1.27045

5 19.3883 0.04110 2003 1 19.42939669 0.04110 0.00212 0.20842

6 7.1798 -0.21703 2003 2 6.962816079 -0.21703 -0.03117 -1.10050

7 2.2013 0.14101 2003 3 2.342267003 0.14101 0.06020 0.71506

8 8.6598 0.30236 2003 4 8.962146091 0.30236 0.03374 1.53320

9 18.1067 0.12075 2004 1 18.22748001 0.12075 0.00662 0.61228

10 6.5734 -0.08672 2004 2 6.486683722 -0.08672 -0.01337 -0.43973

11 2.2878 0.05937 2004 3 2.34720893 0.05937 0.02530 0.30108

12 8.7240 -0.27398 2004 4 8.449978204 -0.27398 -0.03242 -1.38932

13 17.4424 0.13228 2005 1 17.57463045 0.13228 0.00753 0.67076

14 6.9986 -0.47380 2005 2 6.52481479 -0.47380 -0.07262 -2.40258

15 2.0352 0.13706 2005 3 2.17229739 0.13706 0.06309 0.69499

16 7.8840 0.01164 2005 4 7.895653581 0.01164 0.00147 0.05901

17 15.5614 0.04262 2006 1 15.60404549 0.04262 0.00273 0.21611

18 5.8745 -0.23641 2006 2 5.63813132 -0.23641 -0.04193 -1.19879

19 2.1631 -0.02328 2006 3 2.139821348 -0.02328 -0.01088 -0.11807

20 6.9085 -0.27165 2006 4 6.636808923 -0.27165 -0.04093 -1.37751

21 16.5645 -0.26799 2007 1 16.29649841 -0.26799 -0.01644 -1.35895

22 6.2471 0.19534 2007 2 6.442464888 0.19534 0.03032 0.99052

23 2.2665 -0.12943 2007 3 2.137090506 -0.12943 -0.06056 -0.65632

24 7.6841 0.09259 2007 4 7.776686959 0.09259 0.01191 0.46949

25 15.9847 -0.25570 2008 1 15.72900703 -0.25570 -0.01626 -1.29659

26 5.7198 0.28748 2008 2 6.00724404 0.28748 0.04786 1.45779

27 2.0318 0.04095 2008 3 2.07277196 0.04095 0.01976 0.20765

28 7.3890 0.30843 2008 4 7.697419665 0.30843 0.04007 1.56400

29 16.7034 0.00012 2009 1 16.7035644 0.00012 0.00001 0.00063

30 5.4578 0.00026 2009 2 5.458036452 0.00026 0.00005 0.00133

31 1.9746 0.14361 2009 3 2.118156992 0.14361 0.06780 0.72821

32 7.2530 -0.16636 2009 4 7.086594748 -0.16636 -0.02347 -0.84357

33 15.1611 0.21124 2010 1 15.37230403 0.21124 0.01374 1.07115

34 4.5097 0.17813 2010 2 4.687794906 0.17813 0.03800 0.90325

35 2.0589 -0.07570 2010 3 1.983188243 -0.07570 -0.03817 -0.38385



Page A-272

FULL MODEL FORECAST



36 7.5100 0.14148 2010 4 7.651516568 0.14148 0.01849 0.71742

37 16.5869 -0.13685 2011 1 16.45001095 -0.13685 -0.00832 -0.69396

38 5.6662 0.23121 2011 2 5.897420791 0.23121 0.03921 1.17245

39 2.0316 -0.05432 2011 3 1.977312973 -0.05432 -0.02747 -0.27543

40 6.4658 -0.15181 2011 4 6.314030702 -0.15181 -0.02404 -0.76980

41 13.0603 0.18135 2012 1 13.24169997 0.18135 0.01370 0.91961

42 4.5592 -0.12488 2012 2 4.434286216 -0.12488 -0.02816 -0.63327

43 1.9024 -0.02312 2012 3 1.879318346 -0.02312 -0.01230 -0.11722

44 7.0819 -0.11574 2012 4 6.966194914 -0.11574 -0.01661 -0.58688

45 15.3312 -0.24105 2013 1 15.09010974 -0.24105 -0.01597 -1.22234

46 5.9450 -0.08025 2013 2 5.864749782 -0.08025 -0.01368 -0.40693

47 1.8270 0.08901 2013 3 1.91606095 0.08901 0.04646 0.45138

48 7.7177 -0.06458 2013 4 7.653101883 -0.06458 -0.00844 -0.32746

49 17.2687 0.23393 2014 1 17.50260217 0.23393 0.01337 1.18624

50 6.0793 0.13147 2014 2 6.210807476 0.13147 0.02117 0.66665

51 2.0424 -0.12070 2014 3 1.921745977 -0.12070 -0.06281 -0.61205

52 6.8583 0.27556 2014 4 7.133852538 0.27556 0.03863 1.39732

53 18.0636 0.18040 2015 1 18.2440138 0.18040 0.00989 0.91476

54 5.7674 0.24984 2015 2 6.017257317 0.24984 0.04152 1.26689

55 1.7530 0.11702 2015 3 1.870065303 0.11702 0.06258 0.59340

56 6.0268 -0.17429 2015 4 5.852507742 -0.17429 -0.02978 -0.88377

57 13.8048 -0.23813 2016 1 13.56670608 -0.23813 -0.01755 -1.20751

58 5.4784 0.07583 2016 2 5.554186858 0.07583 0.01365 0.38455

59 1.9141 -0.12592 2016 3 1.788161061 -0.12592 -0.07042 -0.63850

60 7.3664 -0.40373 2016 4 6.962640061 -0.40373 -0.05799 -2.04726

61 14.4931 -0.07982 2017 1 14.41326538 -0.07982 -0.00554 -0.40475

62 6.0601 -0.16750 2017 2 5.89263799 -0.16750 -0.02843 -0.84939

63 1.7864 0.04394 2017 3 1.830334616 0.04394 0.02400 0.22279

64 6.8975 -0.05762 2017 4 6.839831926 -0.05762 -0.00842 -0.29220

65 15.7370 0.03083 2018 1 15.7678136 0.03083 0.00195 0.15631

66 6.1388 0.01685 2018 2 6.155688645 0.01685 0.00274 0.08542

67 1.9384 -0.24744 2018 3 1.690961354 -0.24744 -0.14633 -1.25475

68 8.0019 0.16765 2018 4 8.169526171 0.16765 0.02052 0.85010



Page A-273

Springfield Residential Heating UPC Ex Post MODEL FORECAST


03:43 Monday, October 28, 2019 10




Page A-274


03:43 Monday, October 28, 2019 11

bs YEAR Q RHUPC XPRED EXDIFF EXPCTDIFF

1 2018 1 15.7678136 15.7260 0.04180 0.00265

2 2018 2 6.155688645 6.1164 0.03934 0.00639

3 2018 3 1.690961354 1.9409 -0.24995 -0.14782

4 2018 4 8.169526171 8.0957 0.07378 0.00903



Page A-275


03:43 Monday, October 28, 2019 12


1 ARHHPxQ1 Parameter Estimate for ARHHPxQ1 -0.1319 -0.1321 (.15%) 0.00020

2 ARHHPxQ2 Parameter Estimate for ARHHPxQ2 -0.0735 -0.0725 1.4% -0.00102

3 ARHHPxQ4 Parameter Estimate for ARHHPxQ4 -0.0632 -0.0610 3.7% -0.00226



6 SMEDDxQ1 Parameter Estimate for SMEDDxQ1 0.0045 0.0045 .18% 0.00001



9 SMEDDxy2008Q2andbefore Parameter Estimate for SMEDDxy2008Q2andbefore 0.0004 0.0004 (2.0%) -0.00001









18 _LIKLHD_ Log-Likelihood 20.3839 18.7637 8.6% 1.62019



21 between2015Q3_and2016Q1 Parameter Estimate for between2015Q3_and2016Q1 -0.3704 -0.3689 .41% -0.00150

22 year2008andafter Parameter Estimate for year2008andafter -0.3117 -0.3038 2.6% -0.00786

23 year2009Q1 Parameter Estimate for year2009Q1 0.5003 0.5093 (1.8%) -0.00898



Page A-276


The CORR Procedure




CUST TREND DQ411A TrendxDQ411A DQ114A TrendxDQ114A

CUST 1.00000

52

-0.99293 <.0001

52

-0.87601 <.0001

52

-0.95164 <.0001

52

-0.87469 <.0001

52

-0.88516 <.0001

52

TREND -0.99293 <.0001

52

1.00000

100

0.72894 <.0001

100

0.98837 <.0001

100

0.80800 <.0001

100

0.97573 <.0001

100

DQ411A -0.87601 <.0001

52

0.72894 <.0001

100

1.00000

100

0.80096 <.0001

100

0.79671 <.0001

100

0.70634 <.0001

100

TrendxDQ411A -0.95164 <.0001

52

0.98837 <.0001

100

0.80096 <.0001

100

1.00000

100

0.83951 <.0001

100

0.97413 <.0001

100

DQ114A -0.87469 <.0001

52

0.80800 <.0001

100

0.79671 <.0001

100

0.83951 <.0001

100

1.00000

100

0.88658 <.0001

100

TrendxDQ114A -0.88516 <.0001

52

0.97573 <.0001

100

0.70634 <.0001

100

0.97413 <.0001

100

0.88658 <.0001

100

1.00000

100

DQ112 0.01358 0.9239

52

-0.08878 0.3797

100

0.05493 0.5873

100

-0.07016 0.4879

100

-0.14651 0.1458

100

-0.12989 0.1977

100

TrendxDQ115A -0.80291 <.0001

52

0.96823 <.0001

100

0.66466 <.0001

100

0.96102 <.0001

100

0.83426 <.0001

100

0.98231 <.0001

100

DQ2Q309 0.14572 0.3026

52

-0.17816 0.0762

100

-0.26139 0.0086

100

-0.20936 0.0366

100

-0.20825 0.0376

100

-0.18463 0.0659

100



DQ112 TrendxDQ115A DQ2Q309

CUST 0.01358 0.9239

52

-0.80291 <.0001

52

0.14572 0.3026

52

TREND -0.08878 0.3797

100

0.96823 <.0001

100

-0.17816 0.0762

100



Page A-277


The CORR Procedure




DQ112 TrendxDQ115A DQ2Q309

DQ411A 0.05493 0.5873

100

0.66466 <.0001

100

-0.26139 0.0086

100

TrendxDQ411A -0.07016 0.4879

100

0.96102 <.0001

100

-0.20936 0.0366

100

DQ114A -0.14651 0.1458

100

0.83426 <.0001

100

-0.20825 0.0376

100

TrendxDQ114A -0.12989 0.1977

100

0.98231 <.0001

100

-0.18463 0.0659

100

DQ112 1.00000

100

-0.12223 0.2257

100

-0.01436 0.8873

100

TrendxDQ115A -0.12223 0.2257

100

1.00000

100

-0.17373 0.0839

100

DQ2Q309 -0.01436 0.8873

100

-0.17373 0.0839

100

1.00000

100



Page A-278

Springfield Residential Non-Heat Customer Count Full Model






Chow 20 9 34 0.69 0.7108



8 -0.002033 -0.01 0.9905

3 -0.014356 -0.09 0.9299

4 -0.101591 -0.70 0.4856

6 -0.262848 -1.64 0.1091

7 0.261452 1.86 0.0700

2 0.241598 1.66 0.1054




Error t Value

1 -0.326197 0.139726 -2.33

5 0.289964 0.139726 2.08



Page A-279





SSE 131263.143 DFE 41

MSE 3202 Root MSE 56.58215

SBC 599.008303 AIC 577.544623

MAE 41.2182054 AICC 584.144623

MAPE 0.34921123 HQC 585.773291





1 0.4328 0.5106 0.2421 0.6227

2 0.9942 0.6083 0.5643 0.7542

3 4.2271 0.2380 1.7462 0.6267

4 9.4083 0.0517 4.2519 0.3730

5 9.5890 0.0878 4.7990 0.4409

6 10.7323 0.0970 5.6836 0.4596

7 15.3090 0.0322 10.7064 0.1519

8 15.6657 0.0474 11.0671 0.1979

9 18.3789 0.0310 11.4077 0.2488

10 24.1766 0.0071 11.6167 0.3115

11 24.7586 0.0099 11.6176 0.3931

12 29.7820 0.0030 14.1705 0.2900



Page A-280




Parameter Estimates



Intercept 1 15143 28.7348 526.99 <.0001

TREND 1 -115.5764 2.1368 -54.09 <.0001

DQ411A 1 500.2467 274.7701 1.82 0.0760

TrendxDQ411A 1 -20.8401 9.9147 -2.10 0.0417

DQ114A 1 -3314 311.1200 -10.65 <.0001

TrendxDQ114A 1 83.7634 10.5540 7.94 <.0001

DQ112 1 -130.4656 54.6129 -2.39 0.0216

TrendxDQ115A 1 -7.1735 1.5487 -4.63 <.0001

DQ2Q309 1 -171.2495 45.8365 -3.74 0.0006


Lag Autocorr

0 1.0000

1 0.3557

2 0.1148

3 0.0042

4 -0.1018

5 -0.3232



Page A-281






Page A-282

FULL MODEL FORECAST



1 15027.47 -57.017 2006 1 14963.33 -64.138 -.004286362 -1.00768

2 14889.08 -6.732 2006 2 14882 -7.077 -.000475552 -0.11897

3 14786.40 -37.165 2006 3 14747.33 -39.070 -.002649267 -0.65684

4 14665.49 43.322 2006 4 14711 45.510 0.003093630 0.76565

5 14582.56 33.914 2007 1 14618 35.437 0.002424183 0.59938

6 14485.42 0.581 2007 2 14486 0.581 0.000040075 0.01026

7 14354.56 -27.886 2007 3 14326.67 -27.886 -.001946407 -0.49283

8 14230.24 56.757 2007 4 14287 56.757 0.003972610 1.00308

9 14116.45 26.221 2008 1 14142.67 26.221 0.001854055 0.46342

10 13984.95 45.383 2008 2 14030.33 45.383 0.003234661 0.80208

11 13875.19 41.482 2008 3 13916.67 41.482 0.002980726 0.73312

12 13772.92 38.406 2008 4 13811.33 38.406 0.002780765 0.67877

13 13638.68 -96.676 2009 1 13542 -96.676 -.007139010 -1.70860

14 13310.03 -12.034 2009 2 13298 -12.034 -.000904960 -0.21268

15 13207.49 28.841 2009 3 13236.33 28.841 0.002178927 0.50972

16 13280.19 18.809 2009 4 13299 18.809 0.001414351 0.33243

17 13163.93 -106.598 2010 1 13057.33 -106.598 -.008163868 -1.88396

18 13051.80 -71.134 2010 2 12980.67 -71.134 -.005479970 -1.25718

19 12936.50 -75.834 2010 3 12860.67 -75.834 -.005896560 -1.34024

20 12803.85 71.477 2010 4 12875.33 71.477 0.005551437 1.26324

21 12728.73 92.269 2011 1 12821 92.269 0.007196719 1.63071

22 12669.70 26.974 2011 2 12696.67 26.974 0.002124532 0.47673

23 12539.98 -24.309 2011 3 12515.67 -24.309 -.001942314 -0.42963

24 12404.43 41.242 2011 4 12445.67 41.242 0.003313741 0.72888

25 12114.62 -27.622 2012 1 12087 -27.622 -.002285253 -0.48817

26 12060.97 -109.971 2012 2 11951 -109.971 -.009201802 -1.94356

27 11884.67 42.659 2012 3 11927.33 42.659 0.003576587 0.75393

28 11804.00 79.998 2012 4 11884 79.998 0.006731536 1.41383

29 11684.76 -33.759 2013 1 11651 -33.759 -.002897530 -0.59664

30 11543.45 -28.453 2013 2 11515 -28.453 -.002470918 -0.50285

31 11444.88 24.118 2013 3 11469 24.118 0.002102923 0.42626

32 11305.27 -10.266 2013 4 11295 -10.266 -.000908911 -0.18144

33 10580.19 82.140 2014 1 10662.33 82.140 0.007703726 1.45169

34 10572.88 -31.552 2014 2 10541.33 -31.552 -.002993149 -0.55763

35 10497.81 -21.483 2014 3 10476.33 -21.483 -.002050654 -0.37968



Page A-283

FULL MODEL FORECAST



36 10414.92 30.755 2014 4 10445.67 30.755 0.002944272 0.54354

37 10114.91 85.755 2015 1 10200.67 85.755 0.008406827 1.51559

38 10063.51 16.819 2015 2 10080.33 16.819 0.001668481 0.29725

39 10003.76 -35.093 2015 3 9968.67 -35.093 -.003520372 -0.62022

40 9930.61 -11.279 2015 4 9919.33 -11.279 -.001137079 -0.19934

41 9867.83 -87.496 2016 1 9780.33 -87.496 -.008946110 -1.54635

42 9760.98 -31.655 2016 2 9729.33 -31.655 -.003253544 -0.55945

43 9721.58 -36.254 2016 3 9685.33 -36.254 -.003743198 -0.64073

44 9681.95 -10.620 2016 4 9671.33 -10.620 -.001098081 -0.18769

45 9634.03 -8.031 2017 1 9626 -8.031 -.000834305 -0.14194

46 9601.89 -28.221 2017 2 9573.67 -28.221 -.002947731 -0.49875

47 9541.95 -0.620 2017 3 9541.33 -0.620 -.000064973 -0.01096

48 9486.50 80.500 2017 4 9567 80.500 0.008414321 1.42271

49 9441.27 -11.274 2018 1 9430 -11.274 -.001195566 -0.19925

50 9352.07 -19.740 2018 2 9332.33 -19.740 -.002115264 -0.34888

51 9277.73 -11.396 2018 3 9266.33 -11.396 -.001229782 -0.20140

52 9207.92 64.755 2018 4 9272.67 64.755 0.006983420 1.14444



Page A-284

Springfield Residential Non-Heat Customer Count Ex Post Model






Page A-285


03:45 Monday, October 28, 2019 10

Obs Year Q CUST XPRED EXDIFF EXPCTDIFF

1 2018 1 9430 9436.66 -6.6563 -.000705866

2 2018 2 9332.33 9352.58 -20.2549 -.002170401

3 2018 3 9266.33 9284.39 -18.0650 -.001949530

4 2018 4 9272.67 9221.66 51.0141 0.005501553



Page A-286


03:45 Monday, October 28, 2019 11




3 DQ114A Parameter Estimate for DQ114A -3313.60 -3352.29 (1.2%) 38.70

4 DQ2Q309 Parameter Estimate for DQ2Q309 -171.25 -180.53 (5.1%) 9.28

5 DQ411A Parameter Estimate for DQ411A 500.25 552.99 (9.5%) -52.75

6 Intercept Intercept Parameter 15143.04 15133.78 .06% 9.27

7 TREND Parameter Estimate for TREND -115.58 -114.79 .69% -0.79

8 TrendxDQ114A Parameter Estimate for TrendxDQ114A 83.76 85.58 (2.1%) -1.82

9 TrendxDQ115A Parameter Estimate for TrendxDQ115A -7.17 -7.31 (1.9%) 0.14

10 TrendxDQ411A Parameter Estimate for TrendxDQ411A -20.84 -23.37 ( 11%) 2.53










20 _SSE_ Sum of Squares Error 131263.14 145547.96 (9.8%) -14284.82



Page A-287

The CORR Procedure




UPC Q1xSMEDD Q4xSMEDD DQ113 ARNHPxDQ315A

UPC 1.00000

52

0.82210 <.0001

52

-0.01286 0.9279

52

0.35237 0.0104

52

-0.17255 0.2212

52

Q1xSMEDD 0.82210 <.0001

52

1.00000

100

-0.33136 0.0008

100

0.16948 0.0918

100

-0.03378 0.7386

100

Q4xSMEDD -0.01286 0.9279

52

-0.33136 0.0008

100

1.00000

100

-0.05783 0.5676

100

0.03992 0.6933

100

DQ113 0.35237 0.0104

52

0.16948 0.0918

100

-0.05783 0.5676

100

1.00000

100

-0.12824 0.2036

100

ARNHPxDQ315A -0.17255 0.2212

52

-0.03378 0.7386

100

0.03992 0.6933

100

-0.12824 0.2036

100

1.00000

100



Page A-288

Springfield Residential Non-Heating UPC Full Model






Chow 24 5 42 1.07 0.3887



5 0.024602 0.15 0.8798

2 0.033309 0.21 0.8365

3 0.043186 0.28 0.7805

8 -0.053843 -0.35 0.7268

7 0.093746 0.91 0.3667

6 0.085751 0.95 0.3464

1 0.123727 1.44 0.1555



Error t Value

4 -0.828964 0.082465 -10.05



Page A-289





SSE 0.35064254 DFE 46

MSE 0.00762 Root MSE 0.08731

SBC -84.03446 AIC -95.741922

MAE 0.06245457 AICC -93.875256

MAPE 3.83552926 HQC -91.253558





1 0.0457 0.8307 0.0349 0.8517

2 0.5821 0.7475 0.4914 0.7822

3 1.3789 0.7105 0.6729 0.8796

4 2.2055 0.6980 1.3564 0.8517

5 2.8306 0.7261 1.9175 0.8604

6 5.9011 0.4344 2.5719 0.8603

7 8.6379 0.2797 4.4182 0.7305

8 9.1037 0.3336 4.8104 0.7776

9 12.9200 0.1663 5.5164 0.7872

10 14.8048 0.1393 6.0902 0.8076

11 16.1386 0.1361 6.4282 0.8433

12 17.3125 0.1382 7.8548 0.7964



Page A-290




Parameter Estimates



Intercept 1 1.3827 0.0730 18.93 <.0001

Q1xSMEDD 1 0.000269 0.0000327 8.25 <.0001

Q4xSMEDD 1 0.000193 0.0000669 2.89 0.0059

DQ113 1 0.3768 0.0672 5.60 <.0001

ARNHPxDQ315A 1 -0.005161 0.002208 -2.34 0.0238



Page A-291






Page A-292

FULL MODEL FORECAST



1 2.27005 -0.10526 2006 1 2.08184475 -0.18820 -0.09040 -1.20565

2 1.38268 0.14744 2006 2 1.646284102 0.26361 0.16012 1.68870

3 1.38268 -0.09335 2006 3 1.215767871 -0.16691 -0.13729 -1.06923

4 1.66914 -0.06239 2006 4 1.55758752 -0.11155 -0.07162 -0.71462

5 2.16025 -0.14543 2007 1 2.014822137 -0.14543 -0.07218 -1.66569

6 1.60120 0.03772 2007 2 1.638915505 0.03772 0.02301 0.43201

7 1.24432 -0.02082 2007 3 1.22349925 -0.02082 -0.01701 -0.23842

8 1.60468 -0.01790 2007 4 1.58678029 -0.01790 -0.01128 -0.20501

9 2.02990 0.03180 2008 1 2.061704049 0.03180 0.01543 0.36428

10 1.59509 0.04289 2008 2 1.637975016 0.04289 0.02618 0.49120

11 1.25072 -0.03338 2008 3 1.217340786 -0.03338 -0.02742 -0.38237

12 1.63374 0.05594 2008 4 1.689675795 0.05594 0.03310 0.64067

13 2.18547 -0.00837 2009 1 2.177103087 -0.00837 -0.00384 -0.09585

14 1.59431 0.09269 2009 2 1.686995789 0.09269 0.05494 1.06160

15 1.24562 -0.01806 2009 3 1.227555523 -0.01806 -0.01472 -0.20690

16 1.67817 -0.01716 2009 4 1.661002331 -0.01716 -0.01033 -0.19660

17 2.12943 -0.02742 2010 1 2.102012433 -0.02742 -0.01304 -0.31401

18 1.63495 -0.06032 2010 2 1.574623652 -0.06032 -0.03831 -0.69092

19 1.25409 -0.08803 2010 3 1.166061333 -0.08803 -0.07549 -1.00822

20 1.68348 0.01248 2010 4 1.695956531 0.01248 0.00736 0.14293

21 2.21431 0.18603 2011 1 2.400333047 0.18603 0.07750 2.13071

22 1.54179 0.16705 2011 2 1.70884728 0.16705 0.09776 1.91339

23 1.20311 0.00186 2011 3 1.20496945 0.00186 0.00154 0.02129

24 1.64541 0.11493 2011 4 1.760344762 0.11493 0.06529 1.31640

25 2.18623 0.05061 2012 1 2.236838752 0.05061 0.02262 0.57962

26 1.65306 -0.08903 2012 2 1.564025605 -0.08903 -0.05693 -1.01978

27 1.23536 -0.11550 2012 3 1.119865049 -0.11550 -0.10314 -1.32289

28 1.77386 0.06222 2012 4 1.836082127 0.06222 0.03389 0.71265

29 2.72755 -0.01007 2013 1 2.71747747 -0.01007 -0.00371 -0.11539

30 1.53301 0.24175 2013 2 1.774757273 0.24175 0.13622 2.76893

31 1.16482 -0.02263 2013 3 1.142180661 -0.02263 -0.01982 -0.25925

32 1.84757 0.02757 2013 4 1.875136786 0.02757 0.01470 0.31572

33 2.46118 -0.01215 2014 1 2.44902662 -0.01215 -0.00496 -0.13920

34 1.70770 -0.03735 2014 2 1.670346152 -0.03735 -0.02236 -0.42781

35 1.18331 -0.01608 2014 3 1.167234136 -0.01608 -0.01378 -0.18418



Page A-293

FULL MODEL FORECAST



36 1.81032 -0.15266 2014 4 1.657656235 -0.15266 -0.09210 -1.74858

37 2.48670 0.04526 2015 1 2.531958195 0.04526 0.01787 0.51835

38 1.62114 -0.02440 2015 2 1.596740384 -0.02440 -0.01528 -0.27951

39 1.09586 0.00181 2015 3 1.097672006 0.00181 0.00165 0.02074

40 1.52008 -0.01809 2015 4 1.501983501 -0.01809 -0.01205 -0.20724

41 2.16217 -0.09066 2016 1 2.071504745 -0.09066 -0.04377 -1.03842

42 1.46246 0.07070 2016 2 1.533165182 0.07070 0.04611 0.80980

43 1.13720 -0.08761 2016 3 1.049594593 -0.08761 -0.08347 -1.00343

44 1.56894 -0.02279 2016 4 1.546150323 -0.02279 -0.01474 -0.26103

45 2.11369 0.09512 2017 1 2.208809474 0.09512 0.04307 1.08953

46 1.48691 0.14353 2017 2 1.630443707 0.14353 0.08803 1.64396

47 1.08782 -0.03199 2017 3 1.055827647 -0.03199 -0.03030 -0.36640

48 1.53529 0.04424 2017 4 1.579526497 0.04424 0.02801 0.50670

49 2.27181 0.05417 2018 1 2.325980912 0.05417 0.02329 0.62046

50 1.56058 0.00116 2018 2 1.561739673 0.00116 0.00074 0.01325

51 1.08296 -0.06598 2018 3 1.016979754 -0.06598 -0.06488 -0.75572

52 1.63882 0.02378 2018 4 1.662592328 0.02378 0.01430 0.27234



Page A-294

Springfield Residential Non-Heating UPC Ex Post Model






Page A-295




1 2018 1 2.325980912 2.26282 0.063163 0.027155

2 2018 2 1.561739673 1.55535 0.006386 0.004089

3 2018 3 1.016979754 1.09025 -0.073267 -0.072044

4 2018 4 1.662592328 1.63001 0.032586 0.019599



Page A-296


03:47 Monday, October 28, 2019 10


1 ARNHPxDQ315A Parameter Estimate for ARNHPxDQ315A -0.00516 -0.00528 (2.2%) 0.000116

2 DQ113 Parameter Estimate for DQ113 0.37682 0.37753 (.19%) -0.000709


4 Q1xSMEDD Parameter Estimate for Q1xSMEDD 0.00027 0.00026 4.2% 0.000011












16 _SSE_ Sum of Squares Error 0.35064 0.35385 (.91%) -0.003210



Page A-297


The CORR Procedure




cust Q1 Q4 enm ENMD1

cust SPRINGFIELD COMMERCIAL & INDUSTRIAL LOW CUSTOMER COUNT

1.00000

40

0.27247 0.0889

40

0.15710 0.3330

40

0.84161 <.0001

40

0.43826 0.0047

40


0.27247 0.0889

40

1.00000

88

-0.33333 0.0015

88

-0.02687 0.8038

88

0.06509 0.5468

88


0.15710 0.3330

40

-0.33333 0.0015

88

1.00000

88

0.02839 0.7929

88

-0.02087 0.8469

88


0.84161 <.0001

40

-0.02687 0.8038

88

0.02839 0.7929

88

1.00000

88

-0.18007 0.0932

88


0.43826 0.0047

40

0.06509 0.5468

88

-0.02087 0.8469

88

-0.18007 0.0932

88

1.00000

88



Page A-298

SPRINGFIELD COMMERCIAL & INDUSTRIAL LOW CUSTOMER COUNT REGRESSION




SPRINGFIELD COMMERCIAL & INDUSTRIAL LOW CUSTOMER COUNT



Chow 24 5 30 0.54 0.7420



6 -0.047119 -0.24 0.8149

8 0.083887 0.46 0.6494

7 0.189879 1.07 0.2935

2 0.188681 1.15 0.2603

1 -0.200802 -1.17 0.2526

5 0.194362 1.25 0.2187

4 -0.297824 -1.92 0.0632




Error t Value

3 0.348549 0.160744 2.17



Page A-299





SSE 112280.468 DFE 34

MSE 3302 Root MSE 57.46622

SBC 455.523713 AIC 445.390436

MAE 40.7354141 AICC 447.935891

MAPE 0.51558612 HQC 449.054309



Observations 40



1 0.6923 0.4054 0.3941 0.5302

2 0.9399 0.6250 0.5247 0.7692

3 1.7993 0.6151 0.7685 0.8570

4 5.5835 0.2325 2.6646 0.6154

5 12.8870 0.0245 7.2409 0.2033

6 14.9613 0.0206 7.4746 0.2792

7 14.9733 0.0363 7.7260 0.3574

8 15.0203 0.0588 8.0285 0.4307

9 16.0398 0.0661 11.4498 0.2461

10 18.8878 0.0417 12.2806 0.2667

11 20.8966 0.0345 18.0617 0.0802

12 22.3523 0.0338 18.7799 0.0940



Page A-300




Parameter Estimates



Intercept 1 2960 156.5017 18.92 <.0001


Q4 1 69.9013 21.3679 3.27 0.0025 QUARTER 4 INTERCEPT SHIFT


ENMD1 1 0.5903 0.0588 10.04 <.0001 INTERACTION TERM FOR NON MFG EMPLOYMENT BETWEEN 2014Q1 AND 2016Q1

AR3 1 0.7296 0.1275 5.72 <.0001



Page A-301






Page A-302

FULL MODEL FORECAST



1 7781.13 -61.402 2009 1 7691.333333 -89.792 -0.011674 -1.06849

2 7504.86 19.018 2009 2 7532.666667 27.811 0.003692 0.33094

3 7506.07 -31.278 2009 3 7460.333333 -45.740 -0.006131 -0.54429

4 7654.51 -10.509 2009 4 7644 -10.509 -0.001375 -0.18287

5 7765.83 -10.835 2010 1 7755 -10.835 -0.001397 -0.18854

6 7578.31 42.025 2010 2 7620.333333 42.025 0.005515 0.73130

7 7514.87 -18.199 2010 3 7496.666667 -18.199 -0.002428 -0.31669

8 7656.08 30.920 2010 4 7687 30.920 0.004022 0.53806

9 7771.19 26.476 2011 1 7797.666667 26.476 0.003395 0.46072

10 7623.15 13.515 2011 2 7636.666667 13.515 0.001770 0.23518

11 7553.96 -32.293 2011 3 7521.666667 -32.293 -0.004293 -0.56195

12 7698.44 57.562 2011 4 7756 57.562 0.007422 1.00167

13 7835.50 42.501 2012 1 7878 42.501 0.005395 0.73959

14 7688.88 69.455 2012 2 7758.333333 69.455 0.008952 1.20863

15 7594.19 33.144 2012 3 7627.333333 33.144 0.004345 0.57676

16 7734.56 -28.557 2012 4 7706 -28.557 -0.003706 -0.49694

17 7845.68 -21.682 2013 1 7824 -21.682 -0.002771 -0.37731

18 7712.22 -12.549 2013 2 7699.666667 -12.549 -0.001630 -0.21837

19 7731.99 -110.661 2013 3 7621.333333 -110.661 -0.014520 -1.92567

20 7877.05 28.953 2013 4 7906 28.953 0.003662 0.50383

21 8140.02 -97.018 2014 1 8043 -97.018 -0.012062 -1.68827

22 7971.17 -74.499 2014 2 7896.666667 -74.499 -0.009434 -1.29639

23 7847.10 -27.101 2014 3 7820 -27.101 -0.003466 -0.47159

24 8093.08 14.918 2014 4 8108 14.918 0.001840 0.25960

25 8233.53 5.467 2015 1 8239 5.467 0.000664 0.09514

26 8073.27 -8.271 2015 2 8065 -8.271 -0.001025 -0.14392

27 7955.58 -21.576 2015 3 7934 -21.576 -0.002719 -0.37546

28 8106.91 52.757 2015 4 8159.666667 52.757 0.006466 0.91805

29 8275.29 9.711 2016 1 8285 9.711 0.001172 0.16899

30 8007.93 141.401 2016 2 8149.333333 141.401 0.017351 2.46059

31 7927.90 50.766 2016 3 7978.666667 50.766 0.006363 0.88341

32 8068.97 -51.306 2016 4 8017.666667 -51.306 -0.006399 -0.89281

33 8083.39 23.274 2017 1 8106.666667 23.274 0.002871 0.40500

34 7973.67 6.325 2017 2 7980 6.325 0.000793 0.11007

35 8011.94 -105.937 2017 3 7906 -105.937 -0.013400 -1.84347



Page A-303

FULL MODEL FORECAST



36 8166.98 -48.652 2017 4 8118.333333 -48.652 -0.005993 -0.84661

37 8273.87 -56.866 2018 1 8217 -56.866 -0.006921 -0.98956

38 8089.11 -7.780 2018 2 8081.333333 -7.780 -0.000963 -0.13538

39 8003.17 4.496 2018 3 8007.666667 4.496 0.000562 0.07824

40 8154.91 119.760 2018 4 8274.666667 119.760 0.014473 2.08400



Page A-304

SPRINGFIELD COMMERCIAL & INDUSTRIAL LOW CUSTOMER COUNT REGRESSION EX POST




SPRINGFIELD COMMERCIAL & INDUSTRIAL LOW CUSTOMER COUNT



Page A-305




1 2018 1 8217 8266.92 -49.922 -0.006076

2 2018 2 8081.333333 8083.93 -2.597 -0.000321

3 2018 3 8007.666667 7985.22 22.442 0.002803

4 2018 4 8274.666667 8096.33 178.336 0.021552



Page A-306


03:48 Monday, October 28, 2019 10


1 ENMD1 Parameter Estimate for ENMD1 0.59 0.61 (3.2%) -0.02



4 Q4 Parameter Estimate for Q4 69.90 55.50 26% 14.40













17 enm Parameter Estimate for enm 19.24 18.88 1.9% 0.36



Page A-307


The CORR Procedure




UPC Q1xSMEDD Q2xSMEDD Q4xSMEDD ACILLP

UPC 1.00000

48

0.91138 <.0001

48

-0.19655 0.1806

48

-0.03529 0.8118

48

0.00347 0.9813

48

Q1xSMEDD 0.91138 <.0001

48

1.00000

96

-0.33083 0.0010

96

-0.33147 0.0010

96

0.03432 0.7399

96

Q2xSMEDD -0.19655 0.1806

48

-0.33083 0.0010

96

1.00000

96

-0.33074 0.0010

96

0.00395 0.9696

96

Q4xSMEDD -0.03529 0.8118

48

-0.33147 0.0010

96

-0.33074 0.0010

96

1.00000

96

-0.02428 0.8144

96

ACILLP 0.00347 0.9813

48

0.03432 0.7399

96

0.00395 0.9696

96

-0.02428 0.8144

96

1.00000

96

LagSDevfromNorm 0.00506 0.9727

48

-0.00168 0.9871

96

-0.02829 0.7844

96

0.00337 0.9740

96

-0.03270 0.7518

96

DQ12007 0.16828 0.2529

48

0.18046 0.0785

96

-0.05895 0.5683

96

-0.05906 0.5676

96

0.26963 0.0079

96

DQ12008 0.18735 0.2023

48

0.17110 0.0956

96

-0.05895 0.5683

96

-0.05906 0.5676

96

0.19562 0.0561

96



LagSDevfromNorm DQ12007 DQ12008

UPC 0.00506 0.9727

48

0.16828 0.2529

48

0.18735 0.2023

48

Q1xSMEDD -0.00168 0.9871

96

0.18046 0.0785

96

0.17110 0.0956

96

Q2xSMEDD -0.02829 0.7844

96

-0.05895 0.5683

96

-0.05895 0.5683

96



Page A-308


The CORR Procedure




LagSDevfromNorm DQ12007 DQ12008

Q4xSMEDD 0.00337 0.9740

96

-0.05906 0.5676

96

-0.05906 0.5676

96

ACILLP -0.03270 0.7518

96

0.26963 0.0079

96

0.19562 0.0561

96

LagSDevfromNorm 1.00000

96

-0.03128 0.7622

96

-0.02039 0.8437

96

DQ12007 -0.03128 0.7622

96

1.00000

96

-0.01053 0.9189

96

DQ12008 -0.02039 0.8437

96

-0.01053 0.9189

96

1.00000

96



Page A-309

Springfield C & I LLF UPC Full Model





SSE 261.162957 DFE 40

MSE 6.52907 Root MSE 2.55521

SBC 248.496998 AIC 233.52739

MAE 1.84191775 AICC 237.219698

MAPE 5.64056403 HQC 239.184427




Chow 20 8 32 1.35 0.2573



1 0.4026 0.5257 0.3449 0.5570

2 2.2788 0.3200 1.2504 0.5352

3 3.9204 0.2702 2.4002 0.4936

4 5.0915 0.2780 3.1083 0.5399

5 9.9782 0.0759 4.2020 0.5207

6 11.4691 0.0749 5.0184 0.5415

7 11.6107 0.1141 5.0184 0.6577

8 11.6115 0.1694 5.0248 0.7549

9 11.6741 0.2323 5.0553 0.8295

10 12.1484 0.2752 5.5896 0.8485

11 13.5118 0.2612 6.5256 0.8361

12 19.5176 0.0768 10.7395 0.5514



Page A-310




Parameter Estimates



Intercept 1 3.0304 5.3343 0.57 0.5731

Q1xSMEDD 1 0.0307 0.000314 97.61 <.0001

Q2xSMEDD 1 0.0265 0.000903 29.30 <.0001

Q4xSMEDD 1 0.0246 0.000621 39.65 <.0001

ACILLP 1 -0.3473 0.1587 -2.19 0.0346

LagSDevfromNorm 1 11.4289 5.1079 2.24 0.0309

DQ12007 1 -17.2471 2.7878 -6.19 <.0001

DQ12008 1 -8.6888 2.7230 -3.19 0.0028



4 -0.013187 -0.08 0.9402

8 -0.014090 -0.08 0.9360

6 0.051809 0.31 0.7614

2 -0.085430 -0.51 0.6122

3 -0.119041 -0.75 0.4608

5 0.148353 0.94 0.3508

1 -0.170091 -1.09 0.2836

7 0.240373 1.55 0.1301



Page A-311






Page A-312

FULL MODEL FORECAST



1 97.050 -0.00000 2007 1 97.05005085 -0.00000 -0.00000 -0.00000

2 40.451 -3.25215 2007 2 37.19888132 -3.25215 -0.08743 -1.27276

3 8.229 0.03096 2007 3 8.259784354 0.03096 0.00375 0.01212

4 48.434 -3.86580 2007 4 44.56837952 -3.86580 -0.08674 -1.51291

5 102.028 0.00000 2008 1 102.0281752 0.00000 0.00000 0.00000

6 38.687 -4.52280 2008 2 34.16456782 -4.52280 -0.13238 -1.77003

7 8.410 0.22086 2008 3 8.630469572 0.22086 0.02559 0.08643

8 51.760 -1.27880 2008 4 50.48122582 -1.27880 -0.02533 -0.50047

9 120.027 -0.05467 2009 1 119.9720031 -0.05467 -0.00046 -0.02139

10 38.217 1.41562 2009 2 39.63244535 1.41562 0.03572 0.55401

11 8.495 2.69261 2009 3 11.1878379 2.69261 0.24067 1.05377

12 51.186 -1.60159 2009 4 49.58446712 -1.60159 -0.03230 -0.62680

13 113.155 -0.15464 2010 1 112.9998711 -0.15464 -0.00137 -0.06052

14 32.783 -0.05266 2010 2 32.72984559 -0.05266 -0.00161 -0.02061

15 10.370 0.82317 2010 3 11.19359715 0.82317 0.07354 0.32215

16 52.060 1.32369 2010 4 53.38359134 1.32369 0.02480 0.51804

17 120.903 -2.05920 2011 1 118.8438422 -2.05920 -0.01733 -0.80588

18 39.586 3.07445 2011 2 42.66027935 3.07445 0.07207 1.20321

19 8.824 2.16538 2011 3 10.98923111 2.16538 0.19705 0.84744

20 46.385 0.61125 2011 4 46.99621798 0.61125 0.01301 0.23922

21 98.783 2.14278 2012 1 100.9256156 2.14278 0.02123 0.83859

22 33.125 -0.30419 2012 2 32.82079484 -0.30419 -0.00927 -0.11905

23 10.575 1.16798 2012 3 11.74285465 1.16798 0.09946 0.45710

24 48.194 3.89390 2012 4 52.08759408 3.89390 0.07476 1.52391

25 111.065 -4.60139 2013 1 106.4640849 -4.60139 -0.04322 -1.80079

26 40.744 -3.97415 2013 2 36.76938396 -3.97415 -0.10808 -1.55532

27 8.190 -0.26462 2013 3 7.92573478 -0.26462 -0.03339 -0.10356

28 54.443 -1.86932 2013 4 52.57348849 -1.86932 -0.03556 -0.73157

29 127.272 0.40708 2014 1 127.6786025 0.40708 0.00319 0.15931

30 42.349 1.88092 2014 2 44.22967497 1.88092 0.04253 0.73611

31 9.491 0.25926 2014 3 9.750682012 0.25926 0.02659 0.10147

32 49.940 0.09467 2014 4 50.03482158 0.09467 0.00189 0.03705

33 133.132 1.92784 2015 1 135.0598778 1.92784 0.01427 0.75448

34 41.141 3.10700 2015 2 44.24790246 3.10700 0.07022 1.21595

35 10.959 -0.93460 2015 3 10.02398958 -0.93460 -0.09324 -0.36576



Page A-313

FULL MODEL FORECAST



36 45.522 -3.78734 2015 4 41.73450713 -3.78734 -0.09075 -1.48221

37 105.230 -3.78737 2016 1 101.442245 -3.78737 -0.03734 -1.48222

38 42.831 -2.46218 2016 2 40.36878272 -2.46218 -0.06099 -0.96359

39 12.272 -2.32632 2016 3 9.946064505 -2.32632 -0.23389 -0.91042

40 51.455 -0.83514 2016 4 50.61959007 -0.83514 -0.01650 -0.32684

41 107.398 1.83188 2017 1 109.229523 1.83188 0.01677 0.71692

42 42.589 1.63098 2017 2 44.22017544 1.63098 0.03688 0.63830

43 9.718 -1.13679 2017 3 8.580951176 -1.13679 -0.13248 -0.44489

44 49.335 1.52935 2017 4 50.8640115 1.52935 0.03007 0.59852

45 116.687 4.00071 2018 1 120.6878828 4.00071 0.03315 1.56571

46 43.468 3.20157 2018 2 46.669279 3.20157 0.06860 1.25296

47 9.681 -1.08032 2018 3 8.600757607 -1.08032 -0.12561 -0.42279

48 57.665 4.77212 2018 4 62.43756043 4.77212 0.07643 1.86761



Page A-314

Springfield C & I LLF UPC Ex Post Model






Page A-315




1 2018 1 120.6878828 116.201 4.48709 0.03718

2 2018 2 46.669279 43.053 3.61584 0.07748

3 2018 3 8.600757607 9.879 -1.27856 -0.14866

4 2018 4 62.43756043 56.997 5.44075 0.08714



Page A-316


03:49 Monday, October 28, 2019 10


1 ACILLP Parameter Estimate for ACILLP -0.347 -0.313 11% -0.0342




5 LagSDevfromNorm Parameter Estimate for LagSDevfromNorm 11.429 10.990 4.0% 0.4391

6 Q1xSMEDD Parameter Estimate for Q1xSMEDD 0.031 0.030 .61% 0.0002
















Page A-317


The CORR Procedure



CUST emf0 LagSDevfromNorm d5 d6 d8 d9

CUST

1.00000

52

0.74866 <.0001

52

-0.46680 0.0005

52

-0.75299 <.0001

52

-0.17358 0.2185

52

0.00544 0.9694

52

0.00731 0.9590

52

emf0 Manufacturing Employment

0.74866 <.0001

52

1.00000

100

-0.01503 0.8820

100

-0.08938 0.3765

100

-0.64481 <.0001

100

0.24735 0.0131

100

0.25367 0.0109

100

LagSDevfromNorm Q4-Q3 EDD Deviation from Normal

-0.46680 0.0005

52

-0.01503 0.8820

100

1.00000

100

0.43870 <.0001

100

0.14331 0.1549

100

0.03429 0.7348

100

0.01918 0.8497

100

d5 Dummy for data Q3 2014 to Q3 2016

-0.75299 <.0001

52

-0.08938 0.3765

100

0.43870 <.0001

100

1.00000

100

-0.30826 0.0018

100

-0.09880 0.3281

100

-0.09884 0.3279

100

d6 Dummy for data Q4 2018

-0.17358 0.2185

52

-0.64481 <.0001

100

0.14331 0.1549

100

-0.30826 0.0018

100

1.00000

100

-0.30793 0.0018

100

-0.30806 0.0018

100

d8 Interaction term for Q4 2008 and Q4 2010 EDD dev

0.00544 0.9694

52

0.24735 0.0131

100

0.03429 0.7348

100

-0.09880 0.3281

100

-0.30793 0.0018

100

1.00000

100

0.99760 <.0001

100

d9 Interaction term for Q4 2008 and Q4 2010 emf

0.00731 0.9590

52

0.25367 0.0109

100

0.01918 0.8497

100

-0.09884 0.3279

100

-0.30806 0.0018

100

0.99760 <.0001

100

1.00000

100



Page A-318

Springfield C & I HLF Customer Count Full Model




SSE 31252.7707 DFE 45

MSE 694.50601 Root MSE 26.35348

SBC 507.956532 AIC 494.297826

MAE 20.5954599 AICC 496.843281

MAPE 1.12903426 HQC 499.534252




Chow 24 7 38 1.70 0.1383



1 0.0361 0.8494 0.0003 0.9855

2 0.1790 0.9144 0.2158 0.8977

3 5.2392 0.1551 2.1322 0.5454

4 10.7453 0.0296 6.5090 0.1642

5 12.5105 0.0284 6.7996 0.2360

6 12.9819 0.0433 8.2091 0.2232

7 17.1292 0.0166 8.7871 0.2683

8 17.2814 0.0273 8.7887 0.3604

9 17.9607 0.0356 8.9563 0.4413

10 20.4414 0.0253 10.2310 0.4205

11 20.5917 0.0379 10.7411 0.4652

12 20.5918 0.0567 10.7543 0.5501



Page A-319




SSE 24453.2934 DFE 44

MSE 555.75667 Root MSE 23.57449

SBC 499.486459 AIC 483.876509

MAE 17.9220238 AICC 487.225347

MAPE 0.98411099 HQC 489.860996



Observations 52

NOTE: Pr<DW is the p-value for testing positive autocorrelation, and Pr>DW is the p-value for testing negative autocorrelation.



1 1.1510 0.2833 0.6289 0.4278

2 1.4884 0.4751 0.7967 0.6714

3 1.4963 0.6831 0.8234 0.8439

4 2.0666 0.7235 1.1453 0.8870

5 5.8784 0.3182 3.0043 0.6993

6 6.3082 0.3896 3.4236 0.7541

7 7.6091 0.3683 4.0368 0.7755

8 15.1426 0.0564 7.4470 0.4893

9 15.1626 0.0866 7.4509 0.5903

10 16.0273 0.0989 7.7968 0.6487

11 16.7704 0.1149 9.3528 0.5894

12 18.8964 0.0911 10.7755 0.5483



Page A-320



Parameter Estimates



Intercept 1 1606 90.3082 17.78 <.0001

emf0 1 31.2800 3.2420 9.65 <.0001 Manufacturing Employment

LagSDevfromNorm 1 -481.5608 75.2366 -6.40 <.0001 Q4-Q3 EDD Deviation from Normal

d5 1 -80.5482 17.5183 -4.60 <.0001 Dummy for data Q3 2014 to Q3 2016

d6 1 -61.4061 24.7548 -2.48 0.0170 Dummy for data Q4 2018

d8 1 368.8423 162.1566 2.27 0.0279 Interaction term for Q4 2008 and Q4 2010 EDD dev

d9 1 -16.3788 6.6780 -2.45 0.0182 Interaction term for Q4 2008 and Q4 2010 emf

AR1 1 -0.5346 0.1412 -3.79 0.0005



Page A-321





Page A-322

FULL MODEL FORECAST


1 1963.38 24.7531 2006 1 1992.666667 29.2890 0.014698 1.04999

2 1972.99 5.3465 2006 2 1978.333333 5.3465 0.002703 0.22679

3 1963.24 9.0934 2006 3 1972.333333 9.0934 0.004610 0.38573

4 2005.13 -37.4665 2006 4 1967.666667 -37.4665 -0.019041 -1.58928

5 1975.04 3.6222 2007 1 1978.666667 3.6222 0.001831 0.15365

6 1978.70 -25.3661 2007 2 1953.333333 -25.3661 -0.012986 -1.07600

7 1962.77 -11.4387 2007 3 1951.333333 -11.4387 -0.005862 -0.48522

8 1956.80 37.1968 2007 4 1994 37.1968 0.018654 1.57784

9 1978.86 9.8026 2008 1 1988.666667 9.8026 0.004929 0.41581

10 1973.57 -11.5722 2008 2 1962 -11.5722 -0.005898 -0.49088

11 1945.41 -17.0809 2008 3 1928.333333 -17.0809 -0.008858 -0.72455

12 1863.76 -2.4295 2008 4 1861.333333 -2.4295 -0.001305 -0.10306

13 1855.98 -4.3163 2009 1 1851.666667 -4.3163 -0.002331 -0.18309

14 1845.82 -30.4886 2009 2 1815.333333 -30.4886 -0.016795 -1.29329

15 1826.77 -23.7704 2009 3 1803 -23.7704 -0.013184 -1.00831

16 1811.56 25.4372 2009 4 1837 25.4372 0.013847 1.07901

17 1833.19 13.4731 2010 1 1846.666667 13.4731 0.007296 0.57151

18 1837.11 -7.7792 2010 2 1829.333333 -7.7792 -0.004252 -0.32998

19 1827.79 -2.4572 2010 3 1825.333333 -2.4572 -0.001346 -0.10423

20 1838.49 31.8398 2010 4 1870.333333 31.8398 0.017024 1.35060

21 1883.73 -3.7335 2011 1 1880 -3.7335 -0.001986 -0.15837

22 1872.26 -22.2578 2011 2 1850 -22.2578 -0.012031 -0.94415

23 1856.29 -43.9526 2011 3 1812.333333 -43.9526 -0.024252 -1.86441

24 1782.21 3.7938 2011 4 1786 3.7938 0.002124 0.16093

25 1796.82 -15.4838 2012 1 1781.333333 -15.4838 -0.008692 -0.65680

26 1794.02 -29.0242 2012 2 1765 -29.0242 -0.016444 -1.23117

27 1783.90 30.4299 2012 3 1814.333333 30.4299 0.016772 1.29080

28 1913.16 18.1767 2012 4 1931.333333 18.1767 0.009411 0.77103

29 1918.78 8.2177 2013 1 1927 8.2177 0.004265 0.34858

30 1915.25 -2.2454 2013 2 1913 -2.2454 -0.001174 -0.09525

31 1904.30 -18.3030 2013 3 1886 -18.3030 -0.009705 -0.77639

32 1808.35 29.6479 2013 4 1838 29.6479 0.016131 1.25763

33 1821.59 9.0743 2014 1 1830.666667 9.0743 0.004957 0.38492

34 1814.96 -0.6285 2014 2 1814.333333 -0.6285 -0.000346 -0.02666

35 1725.42 35.5813 2014 3 1761 35.5813 0.020205 1.50931



Page A-323

FULL MODEL FORECAST


36 1690.24 -13.5734 2014 4 1676.666667 -13.5734 -0.008095 -0.57577

37 1670.53 10.7990 2015 1 1681.333333 10.7990 0.006423 0.45808

38 1672.01 -10.0098 2015 2 1662 -10.0098 -0.006023 -0.42460

39 1661.70 -13.6986 2015 3 1648 -13.6986 -0.008312 -0.58108

40 1666.28 -23.2800 2015 4 1643 -23.2800 -0.014169 -0.98751

41 1656.65 -22.9846 2016 1 1633.666667 -22.9846 -0.014069 -0.97498

42 1651.48 -28.8157 2016 2 1622.666667 -28.8157 -0.017758 -1.22232

43 1645.42 51.5847 2016 3 1697 51.5847 0.030398 2.18816

44 1851.97 23.0268 2016 4 1875 23.0268 0.012281 0.97677

45 1857.68 27.3198 2017 1 1885 27.3198 0.014493 1.15887

46 1862.83 11.1684 2017 2 1874 11.1684 0.005960 0.47375

47 1858.11 -11.7795 2017 3 1846.333333 -11.7795 -0.006380 -0.49967

48 1802.63 17.0331 2017 4 1819.666667 17.0331 0.009361 0.72252

49 1811.53 19.4701 2018 1 1831 19.4701 0.010634 0.82590

50 1818.47 -4.8023 2018 2 1813.666667 -4.8023 -0.002648 -0.20371

51 1808.99 -37.3190 2018 3 1771.666667 -37.3190 -0.021064 -1.58302

52 1710.67 -0.0000 2018 4 1710.666667 -0.0000 -0.000000 -0.00000



Page A-324

Springfield C&I HLF Customer Count Ex Post MODEL FORECAST





Page A-325


Obs EXPRED YEAR Q CUST EXDIFF EXPCTDIFF

1 1812.74 2018 1 1831 18.2606 0.009973

2 1809.90 2018 2 1813.666667 3.7689 0.002078

3 1808.16 2018 3 1771.666667 -36.4955 -0.020600

4 1792.90 2018 4 1710.666667 -82.2330 -0.048071



Page A-326





3 LagSDevfromNorm Parameter Estimate for LagSDevfromNorm -481.56 -477.29 .90% -4.27

4 _A_1 Parameter Estimate for _A_1 -0.53 -0.54 (.10%) 0.00









13 _MSE_ Estimate of Variance 555.76 551.98 .68% 3.77


15 d5 Parameter Estimate for d5 -80.55 -82.62 (2.5%) 2.07

16 d6 Parameter Estimate for d6 -61.41 . . .



19 emf0 Parameter Estimate for emf0 31.28 30.76 1.7% 0.52



Page A-327


The CORR Procedure



CIHLFUPC ACIHLP smedd1 smedd2 smedd4 IP0 rgcp4 d5

CIHLFUPC

1.00000

52

-0.54599 <.0001

52

0.45639 0.0007

52

0.03648 0.7974

52

0.00380 0.9787

52

-0.01796 0.8994

52

0.71580 <.0001

48

0.75425 <.0001

52

ACIHLP

-0.54599 <.0001

52

1.00000

140

0.03000 0.7670

100

0.00904 0.9289

100

-0.03857 0.7032

100

-0.36393 <.0001

140

-0.55712 <.0001

136

-0.82924 <.0001

140

smedd1

0.45639 0.0007

52

0.03000 0.7670

100

1.00000

100

-0.33091 0.0008

100

-0.33136 0.0008

100

-0.02491 0.8056

100

-0.03012 0.7708

96

-0.02114 0.8346

100

smedd2

0.03648 0.7974

52

0.00904 0.9289

100

-0.33091 0.0008

100

1.00000

100

-0.33067 0.0008

100

-0.00556 0.9562

100

-0.00493 0.9620

96

-0.00358 0.9718

100

smedd4

0.00380 0.9787

52

-0.03857 0.7032

100

-0.33136 0.0008

100

-0.33067 0.0008

100

1.00000

100

0.03655 0.7181

100

0.04694 0.6497

96

0.05077 0.6159

100

IP0

-0.01796 0.8994

52

-0.36393 <.0001

140

-0.02491 0.8056

100

-0.00556 0.9562

100

0.03655 0.7181

100

1.00000

172

0.94996 <.0001

168

0.59955 <.0001

172

rgcp4

0.71580 <.0001

48

-0.55712 <.0001

136

-0.03012 0.7708

96

-0.00493 0.9620

96

0.04694 0.6497

96

0.94996 <.0001

168

1.00000

168

0.66029 <.0001

168

d5 Dummy for data after q3 2015

0.75425 <.0001

52

-0.82924 <.0001

140

-0.02114 0.8346

100

-0.00358 0.9718

100

0.05077 0.6159

100

0.59955 <.0001

172

0.66029 <.0001

168

1.00000

172



Page A-328

Springfield C&I HLF UPC Full Model




SSE 4246.32843 DFE 40

MSE 106.15821 Root MSE 10.30331

SBC 382.352938 AIC 367.38333

MAE 7.09524855 AICC 371.075638

MAPE 3.92496148 HQC 373.040367




Chow 18 8 32 2.50 0.0312



1 5.7061 0.0169 1.5661 0.2108

2 15.7987 0.0004 3.8757 0.1440

3 16.4483 0.0009 3.8758 0.2752

4 19.5095 0.0006 4.0623 0.3976

5 25.7329 0.0001 5.9839 0.3078

6 36.8795 <.0001 6.7398 0.3456

7 39.5727 <.0001 6.7596 0.4543

8 39.7155 <.0001 7.2756 0.5072

9 40.2338 <.0001 7.5942 0.5755

10 40.5318 <.0001 8.7056 0.5603

11 40.7670 <.0001 8.7409 0.6458

12 41.0025 <.0001 9.1392 0.6910



Page A-329




SSE 3087.79571 DFE 37

MSE 83.45394 Root MSE 9.13531

SBC 379.588604 AIC 359.005393

MAE 6.3223425 AICC 366.338726

MAPE 3.51830321 HQC 366.783818



Observations 48



1 1.6210 0.2029 0.4511 0.5018

2 1.8118 0.4042 0.4985 0.7794

3 2.2402 0.5241 0.5216 0.9141

4 3.0395 0.5512 0.6756 0.9543

5 5.5425 0.3533 1.9449 0.8567

6 17.0051 0.0093 5.9034 0.4341

7 17.5033 0.0144 5.9693 0.5433

8 17.8913 0.0221 6.3923 0.6034

9 18.0737 0.0343 6.4898 0.6901

10 18.1715 0.0521 7.9125 0.6374



Page A-330





11 19.1525 0.0584 8.5588 0.6625

12 19.2314 0.0831 9.2561 0.6809

Parameter Estimates



Intercept 1 -266.1735 80.4399 -3.31 0.0021

ACIHLP 1 -1.3043 0.6933 -1.88 0.0678

smedd1 1 0.0188 0.001071 17.60 <.0001

smedd2 1 0.0288 0.002582 11.15 <.0001

smedd4 1 0.0158 0.001762 8.94 <.0001

IP0 1 1.6723 0.4254 3.93 0.0004

rgcp4 1 0.0110 0.003749 2.92 0.0059

d5 1 49.6668 4.9940 9.95 <.0001 Dummy for data after q3 2015

AR1 1 -0.2820 0.1505 -1.87 0.0689

AR2 1 0.3820 0.1529 2.50 0.0170

AR4 1 0.3653 0.1505 2.43 0.0202



Page A-331





Page A-332

FULL MODEL FORECAST


1 201.278 -7.8823 2007 1 191.9058288 -9.3725 -0.04884 -0.86284

2 169.346 -2.8641 2007 2 166.0716724 -3.2743 -0.01972 -0.31352

3 138.438 -16.4037 2007 3 120.6902972 -17.7475 -0.14705 -1.79564

4 161.554 2.7253 2007 4 164.4816115 2.9277 0.01780 0.29833

5 211.718 12.2236 2008 1 223.9415018 12.2236 0.05458 1.33807

6 177.743 -1.6971 2008 2 176.0455318 -1.6971 -0.00964 -0.18577

7 129.882 -0.0482 2008 3 129.8340536 -0.0482 -0.00037 -0.00528

8 148.462 6.0306 2008 4 154.4921203 6.0306 0.03903 0.66014

9 177.907 -18.6357 2009 1 159.2714671 -18.6357 -0.11701 -2.03996

10 136.283 8.8831 2009 2 145.1663606 8.8831 0.06119 0.97239

11 127.720 8.7456 2009 3 136.4655204 8.7456 0.06409 0.95734

12 152.731 -2.7831 2009 4 149.9481038 -2.7831 -0.01856 -0.30466

13 190.724 -7.5427 2010 1 183.1814079 -7.5427 -0.04118 -0.82566

14 149.162 -3.2969 2010 2 145.8649781 -3.2969 -0.02260 -0.36089

15 120.843 3.4863 2010 3 124.3290723 3.4863 0.02804 0.38163

16 155.311 -1.6387 2010 4 153.6720727 -1.6387 -0.01066 -0.17938

17 198.769 8.4842 2011 1 207.2537234 8.4842 0.04094 0.92873

18 166.569 -0.2285 2011 2 166.3401802 -0.2285 -0.00137 -0.02501

19 128.232 4.9620 2011 3 133.1940408 4.9620 0.03725 0.54316

20 154.189 8.9792 2011 4 163.1681598 8.9792 0.05503 0.98292

21 185.340 -5.7558 2012 1 179.5843937 -5.7558 -0.03205 -0.63006

22 152.416 -0.3698 2012 2 152.0464589 -0.3698 -0.00243 -0.04048

23 135.813 -8.4365 2012 3 127.3760794 -8.4365 -0.06623 -0.92350

24 160.874 -5.0543 2012 4 155.8199862 -5.0543 -0.03244 -0.55327

25 206.766 -4.3936 2013 1 202.3724269 -4.3936 -0.02171 -0.48095

26 181.439 -4.5043 2013 2 176.9348319 -4.5043 -0.02546 -0.49306

27 144.244 -7.5810 2013 3 136.6626016 -7.5810 -0.05547 -0.82986

28 170.910 8.9776 2013 4 179.8875589 8.9776 0.04991 0.98274

29 218.773 1.3848 2014 1 220.157866 1.3848 0.00629 0.15159

30 175.537 -0.6271 2014 2 174.9101598 -0.6271 -0.00359 -0.06865

31 141.230 0.2941 2014 3 141.5243233 0.2941 0.00208 0.03220

32 165.491 8.9242 2014 4 174.415507 8.9242 0.05117 0.97689

33 217.473 -4.7315 2015 1 212.7416733 -4.7315 -0.02224 -0.51794

34 172.510 0.4312 2015 2 172.941436 0.4312 0.00249 0.04720

35 144.782 -4.6759 2015 3 140.1057848 -4.6759 -0.03337 -0.51185



Page A-333

FULL MODEL FORECAST


36 213.739 4.0844 2015 4 217.822885 4.0844 0.01875 0.44710

37 257.491 16.0921 2016 1 273.5827382 16.0921 0.05882 1.76153

38 237.961 14.1576 2016 2 252.1185292 14.1576 0.05615 1.54977

39 199.572 3.9975 2016 3 203.5694363 3.9975 0.01964 0.43759

40 219.638 -10.9136 2016 4 208.7248 -10.9136 -0.05229 -1.19466

41 246.968 11.4527 2017 1 258.4208665 11.4527 0.04432 1.25367

42 236.121 -5.0789 2017 2 231.0423337 -5.0789 -0.02198 -0.55596

43 199.290 -2.0384 2017 3 197.2514894 -2.0384 -0.01033 -0.22314

44 236.529 -20.0147 2017 4 216.5141967 -20.0147 -0.09244 -2.19092

45 268.455 -1.8908 2018 1 266.5641726 -1.8908 -0.00709 -0.20698

46 247.337 -7.9446 2018 2 239.3923911 -7.9446 -0.03319 -0.86966

47 208.261 10.2329 2018 3 218.4935089 10.2329 0.04683 1.12015

48 244.982 1.8915 2018 4 246.8733437 1.8915 0.00766 0.20705



Page A-334

Springfield C&I HLF UPC Ex Post Model





Page A-335


Obs YEAR Q CIHLFUPC EXpupc EXDIFF EXPCTDIFF

1 2018 1 266.5641726 268.432 -1.8674 -0.007005

2 2018 2 239.3923911 248.679 -9.2862 -0.038791

3 2018 3 218.4935089 208.469 10.0243 0.045879

4 2018 4 246.8733437 237.789 9.0843 0.036798



Page A-336



1 ACIHLP Parameter Estimate for ACIHLP -1.30 -1.37 (5.1%) 0.070


3 IP0 Parameter Estimate for IP0 1.67 1.66 .70% 0.012

4 Intercept Intercept Parameter -266.17 -256.00 4.0% -10.170



7 _A_4 Parameter Estimate for _A_4 0.37 0.37 (.71%) -0.003




11 d5 Parameter Estimate for d5 49.67 49.58 .18% 0.089

12 rgcp4 Parameter Estimate for rgcp4 0.01 0.01 3.7% 0.000

13 smedd1 Parameter Estimate for smedd1 0.02 0.02 (1.4%) -0.000

14 smedd2 Parameter Estimate for smedd2 0.03 0.03 (4.5%) -0.001

15 smedd4 Parameter Estimate for smedd4 0.02 0.02 (.54%) -0.000



Page A-337


The CORR Procedure




DTH SMEDD

DTH 1.00000

12

0.96975 <.0001

12

SMEDD 0.96975 <.0001

12

1.00000

60



Page A-338

Springfield Capacity Exempt Volumes Full Model





SSE 4390211169 DFE 10

MSE 439021117 Root MSE 20953

SBC 275.637176 AIC 274.667363

MAE 16782.1627 AICC 276.000696

MAPE 5.67328341 HQC 274.308303




Chow 6 2 8 1.00 0.4080



1 8.5084 0.0035 1.8787 0.1705

2 8.6207 0.0134 3.4222 0.1807

3 9.7087 0.0212 3.8387 0.2794

4 9.8285 0.0434 3.8535 0.4262

5 14.0458 0.0153 5.3933 0.3698

6 19.1988 0.0038 5.9764 0.4258

7 23.0536 0.0017 6.1901 0.5177

8 27.4895 0.0006 6.9616 0.5408

9 27.6218 0.0011 8.7297 0.4626

10 29.6796 0.0010 10.3889 0.4071

11 33.6586 0.0004 12.0000 0.3636

12 33.6586 0.0008 . .



Page A-339




Parameter Estimates



Intercept 1 212770 10180 20.90 <.0001

SMEDD 1 66.0315 5.2558 12.56 <.0001



4 -0.157381 -0.20 0.8634

8 -0.181500 -0.32 0.7702

7 0.115660 0.23 0.8277

6 -0.170422 -0.41 0.6973

5 0.168530 0.47 0.6542

3 -0.267818 -0.74 0.4860

2 0.342655 1.03 0.3324

1 -0.303982 -0.96 0.3634



Page A-340






Page A-341

FULL MODEL FORECAST



1 413470.19 -27521.86 2016 1 385948.3333 -27521.86 -0.07131 -1.31352

2 289066.82 -23286.12 2016 2 265780.7 -23286.12 -0.08761 -1.11136

3 214268.13 -18372.99 2016 3 195895.1333 -18372.99 -0.09379 -0.87687

4 323562.29 11914.91 2016 4 335477.2 11914.91 0.03552 0.56865

5 422930.71 9478.62 2017 1 432409.3333 9478.62 0.02192 0.45238

6 294808.56 -6051.10 2017 2 288757.4667 -6051.10 -0.02096 -0.28880

7 217563.70 -8819.27 2017 3 208744.4333 -8819.27 -0.04225 -0.42091

8 316707.01 19054.02 2017 4 335761.0333 19054.02 0.05675 0.90938

9 442419.01 -4274.54 2018 1 438144.4667 -4274.54 -0.00976 -0.20401

10 297107.66 -12367.09 2018 2 284740.5667 -12367.09 -0.04343 -0.59023

11 215264.60 24750.77 2018 3 240015.3667 24750.77 0.10312 1.18126

12 340589.41 35494.65 2018 4 376084.0667 35494.65 0.09438 1.69403



Page A-342

Springfield Capacity Exempt Volumes Ex Post Model






Page A-343




1 2018 1 438144.4667 442362.70 -4218.23 -0.00963

2 2018 2 284740.5667 291062.03 -6321.47 -0.02220

3 2018 3 240015.3667 205845.64 34169.73 0.14236

4 2018 4 376084.0667 336335.98 39748.09 0.10569



Page A-344






3 SMEDD Parameter Estimate for SMEDD 66.03 68.75 (4.0%) -2.72













Page A-345

Appendix 10: Company Use Gas

The Company Use gas forecast was developed by division using ten years of actual data.

The annual level was set equal to the ten year average while the monthly levels were calculated

with an allocation percentage. An allocation percentage was used rather than the calculated

average of the monthly values because of the variability of the data. The allocation percentage

was calculated as the percent occurring in a given month of the ten year average annual level

calculated excluding observations that were more than two standard deviations from the average

of the monthly values.

These tables present all data with the forecast, allocation percentages and excluded observations shaded in gray.

Company Use - Dth

Brockton2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 Average Std Dev *2 Forecast

Jan 3,215 1,982 2,698 2,691 3,076 3,119 2,544 5,302 2,619 3,484 3,073 1,779 2,940 Feb 3,268 2,231 3,325 2,812 4,670 2,894 2,831 6,120 2,515 2,717 3,338 2,360 3,152 Mar 3,154 1,090 2,175 2,759 1,593 2,194 6,440 3,248 2,375 3,878 2,890 2,980 2,597 Apr 2,694 572 2,047 1,637 968 1,147 2,783 1,235 2,350 2,374 1,781 1,553 1,853 May 1,558 497 1,117 1,875 588 617 1,271 817 1,308 1,625 1,127 964 1,173 Jun 2,949 1,149 1,991 2,554 872 819 974 1,437 1,263 1,942 1,595 1,472 1,660 Jul 3,831 24 2,092 2,302 1,773 1,285 3,154 1,839 1,781 1,690 1,977 2,046 2,057 Aug 1,151 2,834 2,224 2,925 1,201 1,142 2,084 2,150 2,156 2,483 2,035 1,329 2,117 Sep 928 - 1,959 2,401 1,028 924 2,333 2,472 1,901 1,895 1,584 1,638 1,648 Oct 1,619 1,723 2,155 1,720 612 615 1,438 2,036 1,884 1,398 1,520 1,067 1,582 Nov 705 2,256 1,901 1,568 844 740 1,309 1,834 1,785 1,999 1,494 1,126 1,555 Dec 1,327 2,437 2,058 2,391 1,459 1,282 2,600 2,435 1,829 2,175 1,999 993 2,080

26,397 16,795 25,741 27,636 18,683 16,778 29,761 30,925 23,766 27,661 24,414 24,414 101,311 83,377 99,678 90,186 87,530 93,634 93,079 86,340 77,806 80,021

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 Average Mon DistJan 3,215 1,982 2,698 2,691 3,076 3,119 2,544 2,619 3,484 2,825 12.0%Feb 3,268 2,231 3,325 2,812 4,670 2,894 2,831 2,515 2,717 3,029 12.9%Mar 3,154 1,090 2,175 2,759 1,593 2,194 3,248 2,375 3,878 2,496 10.6%Apr 2,694 572 2,047 1,637 968 1,147 2,783 1,235 2,350 2,374 1,781 7.6%May 1,558 497 1,117 1,875 588 617 1,271 817 1,308 1,625 1,127 4.8%Jun 2,949 1,149 1,991 2,554 872 819 974 1,437 1,263 1,942 1,595 6.8%Jul 3,831 24 2,092 2,302 1,773 1,285 3,154 1,839 1,781 1,690 1,977 8.4%Aug 1,151 2,834 2,224 2,925 1,201 1,142 2,084 2,150 2,156 2,483 2,035 8.7%Sep 928 - 1,959 2,401 1,028 924 2,333 2,472 1,901 1,895 1,584 6.8%Oct 1,619 1,723 2,155 1,720 612 615 1,438 2,036 1,884 1,398 1,520 6.5%Nov 705 2,256 1,901 1,568 844 740 1,309 1,834 1,785 1,999 1,494 6.4%Dec 1,327 2,437 2,058 2,391 1,459 1,282 2,600 2,435 1,829 2,175 1,999 8.5%

26,397 16,795 25,741 27,636 18,683 16,778 23,321 19,503 23,766 27,661 23,463 100.0%



Page A-346

Company Use - Dth

Lawrence2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 Average Std Dev *2 Forecast

Jan 3,310 3,238 3,940 3,763 3,245 3,654 4,562 3,976 3,733 4,110 3,753 842 3,714 Feb 3,464 3,166 3,574 3,067 3,832 4,309 4,873 3,528 3,621 4,036 3,747 1,085 3,584 Mar 2,945 3,416 3,463 3,582 3,019 4,163 4,337 3,436 3,545 4,068 3,597 926 3,560 Apr 3,257 3,007 3,474 3,193 3,124 3,883 3,956 3,357 3,832 3,677 3,476 683 3,440 May 2,474 2,741 1,747 1,732 2,595 2,855 2,817 2,611 3,035 3,150 2,576 970 2,549 Jun 2,131 1,853 1,378 1,426 2,329 3,032 2,404 2,253 2,804 2,551 2,216 1,083 2,193 Jul 2,021 1,700 1,413 1,670 1,511 2,990 2,170 2,770 2,318 2,230 2,079 1,049 2,058 Aug 2,441 1,587 1,649 1,567 1,652 2,295 2,427 2,391 1,765 2,249 2,002 771 1,981 Sep 2,475 2,136 1,881 1,821 1,637 2,913 2,287 2,267 1,707 2,571 2,169 826 2,147 Oct 2,011 1,906 2,135 1,614 1,648 2,804 1,943 2,270 1,952 336 1,862 1,265 2,010 Nov 2,092 2,641 2,245 2,322 2,365 2,760 2,792 2,288 2,281 720 2,251 1,173 2,395 Dec 2,133 2,888 2,784 3,674 2,826 3,289 2,792 2,971 3,067 1,653 2,808 1,130 2,905

30,755 30,279 29,682 29,431 29,783 38,947 37,359 34,117 33,658 31,350 32,536 32,536

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 Average Mon DistJan 3,310 3,238 3,940 3,763 3,245 3,654 4,562 3,976 3,733 4,110 3,753 11.4%Feb 3,464 3,166 3,574 3,067 3,832 4,309 3,528 3,621 4,036 3,622 11.0%Mar 2,945 3,416 3,463 3,582 3,019 4,163 4,337 3,436 3,545 4,068 3,597 10.9%Apr 3,257 3,007 3,474 3,193 3,124 3,883 3,956 3,357 3,832 3,677 3,476 10.6%May 2,474 2,741 1,747 1,732 2,595 2,855 2,817 2,611 3,035 3,150 2,576 7.8%Jun 2,131 1,853 1,378 1,426 2,329 3,032 2,404 2,253 2,804 2,551 2,216 6.7%Jul 2,021 1,700 1,413 1,670 1,511 2,990 2,170 2,770 2,318 2,230 2,079 6.3%Aug 2,441 1,587 1,649 1,567 1,652 2,295 2,427 2,391 1,765 2,249 2,002 6.1%Sep 2,475 2,136 1,881 1,821 1,637 2,913 2,287 2,267 1,707 2,571 2,169 6.6%Oct 2,011 1,906 2,135 1,614 1,648 2,804 1,943 2,270 1,952 2,031 6.2%Nov 2,092 2,641 2,245 2,322 2,365 2,760 2,792 2,288 2,281 2,421 7.4%Dec 2,133 2,888 2,784 3,674 2,826 3,289 2,792 2,971 3,067 2,936 8.9%

30,755 30,279 29,682 29,431 29,783 38,947 32,487 34,117 33,658 28,641 32,879 100.0%



Page A-347

Company Use - Dth

Springfield2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 Average Std Dev *2 Forecast

Jan 9,628 8,919 7,914 6,821 8,175 8,938 4,040 3,411 3,668 4,533 6,605 4,892 6,700 Feb 6,288 8,041 8,084 6,109 10,666 9,762 4,985 3,460 3,866 3,623 6,488 5,157 6,582 Mar 8,586 6,234 7,494 5,721 4,128 5,155 4,440 3,835 3,019 2,724 5,134 3,801 5,208 Apr 4,794 2,622 5,657 3,630 3,686 3,818 5,062 3,042 2,757 2,491 3,756 2,192 3,810 May 1,869 1,647 2,590 2,346 1,684 1,983 1,388 1,437 1,126 1,297 1,736 935 1,762 Jun 913 1,312 1,322 862 1,174 1,045 725 988 675 366 938 598 952 Jul 818 826 774 498 721 779 529 379 440 325 609 389 618 Aug 616 443 416 248 663 644 338 268 304 166 410 356 416 Sep 648 407 618 623 431 597 308 144 295 201 427 375 433 Oct 841 899 1,367 1,209 1,085 712 624 404 610 397 815 658 826 Nov 3,147 1,684 3,564 2,091 1,709 1,533 1,324 1,237 1,025 1,552 1,887 1,664 1,725 Dec 6,012 3,269 4,456 2,963 4,944 2,943 2,196 2,693 2,596 3,334 3,541 2,416 3,313

44,159 36,303 44,255 33,120 39,064 37,909 25,959 21,298 20,382 21,010 32,346 - 32,346

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 Average Mon DistJan 9,628 8,919 7,914 6,821 8,175 8,938 4,040 3,411 3,668 4,533 6,605 21%Feb 6,288 8,041 8,084 6,109 10,666 9,762 4,985 3,460 3,866 3,623 6,488 20%Mar 8,586 6,234 7,494 5,721 4,128 5,155 4,440 3,835 3,019 2,724 5,134 16%Apr 4,794 2,622 5,657 3,630 3,686 3,818 5,062 3,042 2,757 2,491 3,756 12%May 1,869 1,647 2,590 2,346 1,684 1,983 1,388 1,437 1,126 1,297 1,736 5%Jun 913 1,312 1,322 862 1,174 1,045 725 988 675 366 938 3%Jul 818 826 774 498 721 779 529 379 440 325 609 2%Aug 616 443 416 248 663 644 338 268 304 166 410 1%Sep 648 407 618 623 431 597 308 144 295 201 427 1%Oct 841 899 1,367 1,209 1,085 712 624 404 610 397 815 3%Nov 3,147 1,684 2,091 1,709 1,533 1,324 1,237 1,025 1,552 1,700 5%Dec 3,269 4,456 2,963 4,944 2,943 2,196 2,693 2,596 3,334 3,266 10%

38,147 36,303 40,691 33,120 39,064 37,909 25,959 21,298 20,382 21,010 31,885 100%



Page A-348

APPENDIX 11. CALCULATION OF NATURAL GAS PRICES

Because economic theory suggests that price is likely to influence demand, an appropriate

natural gas price variable that reflects the price that CMA customers pay for natural gas was developed

to be tested in the use per customer models. Historical natural gas prices were developed from

Company data by dividing the quarterly Customer Segment revenues by quarterly Customer Segment

volume; the calculated values represent the full cost to customers of natural gas “at the burnertip”.

The same gas price variable was used for all divisions. All nominal historical prices were converted to

real 2018 dollars using a consumer price index (“CPI”) from the Department of Energy, Energy

Information Administration (“EIA”). To develop forecasted gas prices by Customer Segment for the

CMA service territory, these CMA historical prices were used as the dependent variables in regression

(econometric) models, with natural gas prices for New England residential, commercial, and industrial

customers compiled using EIA data as the independent variables.

Natural gas prices used for the independent variables in the regression models were developed

using a combination of: (1) the EIA Short Term Energy Outlook (“STEO”),1 which provides quarterly

history and approximately two years of quarterly forecasts, and (2) EIA’s 2019 Annual Energy Outlook

with Projections to 2050 (“AEO”).2 The STEO and AEO both provide New England natural gas

prices for residential, commercial, and industrial customer classes. All EIA prices were converted to

2018 dollars using CPI. To produce quarterly EIA New England gas prices through the end of the

forecast period that were used as the independent variables in the gas price regressions, the quarterly

STEO data was used through 2020, and the appropriate forecasted annual growth rate from the AEO

was applied from 2021 through the end of the forecast period to develop the longer-term price

forecast.

Econometric forecasts of natural gas prices by Customer Segment for the CMA service

territory as a function of quarterly EIA New England gas prices and various time-specific dummy

variables and AR terms were developed. EIA New England gas prices for residential customers were

used to forecast quarterly Residential Heat and Residential Non-Heat gas prices for CMA. EIA New

England gas prices for commercial customers were used to forecast quarterly LLF C&I gas prices for

CMA. EIA New England gas prices for industrial customers were used to forecast quarterly HLF

C&I gas prices for CMA.

1 Dated February 18, 2019, Table 5b, U.S. Regional Natural Gas Prices. 2 Dated January 2019, Energy Prices by Sector and Source.



Page A-349

The price variable that was used in the use per customer models was determined by calculating

rolling four quarter averages from the historical and forecasted quarterly natural gas prices resulting

from the regression models. The price variable, measured as rolling four quarter averages, reflects the

concept that gas equipment purchases and changes in gas usage behavior are customer decisions that

occur over an extended twelve-month period.3 The following graphs illustrate the quarterly historical

and forecast natural gas prices and the rolling four quarters price variable that was used in the use per

customer models.

Residential Natural Gas Prices

3 A price variable that is calculated as rolling four quarter averages also avoids a statistical problem with data known as

“simultaneity,” which occurs when two variables have an effect on each other at the same time. For example, the price of gas service, measured as average revenues per therm may be generally higher in the summer, and lower in the winter because of the impact of fixed customer charges on the average rate, divided by low delivery quantities in the summer and high delivery quantities in the winter. Simultaneity occurs because in this example, a high price did not cause low usage; rather, a high price was caused by low usage.



Page A-350

C&I Natural Gas Prices



Page A-351

Appendix 12: Annual Summary of Results

Brockton

Customer Count by Segment

Customer Count by Segment – Annual % Growth Rates


Residential Heat C&I LLF C&I HLF Total

2013-2014 9,238 127,510 13,856 2,783 153,387 2014-2015 8,658 130,192 14,101 2,627 155,578

Historical Actual 2015-2016 8,337 132,665 14,317 2,609 157,928 2016-2017 8,136 134,962 14,088 3,036 160,222 2017-2018 7,883 137,782 14,353 2,925 162,944

Partial Historical (Nov-Dec)/ Partial Forecast (Jan - Oct) 2018-2019 7,638 139,944 14,582 2,642 164,807

2019-2020 7,384 142,003 14,686 2,648 166,722 2020-2021 7,131 143,961 14,727 2,644 168,463

Forecast 2021-2022 6,878 145,895 14,727 2,640 170,140 2022-2023 6,625 147,842 14,705 2,637 171,810 2023-2024 6,372 149,781 14,693 2,633 173,479



2013-2014 N/A N/A N/A N/A N/A2014-2015 -6.29% 2.10% 1.77% -5.59% 1.43%

Historical Actual 2015-2016 -3.71% 1.90% 1.53% -0.69% 1.51%2016-2017 -2.40% 1.73% -1.60% 16.36% 1.45%2017-2018 -3.11% 2.09% 1.88% -3.67% 1.70%

Partial Historical (Nov-Dec)/ Partial Forecast (Jan - Oct) 2018-2019 -3.11% 1.57% 1.59% -9.65% 1.14%

2019-2020 -3.32% 1.47% 0.72% 0.22% 1.16%2020-2021 -3.43% 1.38% 0.28% -0.16% 1.04%

Forecast 2021-2022 -3.55% 1.34% 0.00% -0.14% 1.00%2022-2023 -3.68% 1.34% -0.15% -0.11% 0.98%2023-2024 -3.82% 1.31% -0.09% -0.16% 0.97%

Historical Average Annual % Growth - 2013-14 to 2017-18 -3.89% 1.96% 0.89% 1.25% 1.52%

Forecast Average Annual % Growth - 2019-20 to 2023-24 -3.62% 1.34% 0.01% -0.14% 1.00%



Page A-352

Normalized Demand by Customer Segment – (Dth)

Normalized Demand by Customer Segment – Annual and Average % Growth Rates


Residential Heat C&I LLF C&I HLF

CE Transportation

Company Use Total Sendout

Total Planning Load

2013-2014 173,725 12,137,855 7,022,224 4,446,199 2,459,023 17,059 23,797,062 21,338,038 2014-2015 150,233 12,345,953 7,626,227 3,778,380 1,845,104 27,874 23,928,667 22,083,562

Historical Actual 2015-2016 141,227 12,347,521 7,466,757 3,802,154 1,746,702 30,565 23,788,225 22,041,522 2016-2017 142,595 12,732,921 6,911,590 4,747,008 1,729,338 24,422 24,558,535 22,829,197 2017-2018 138,267 13,423,977 7,892,364 4,380,214 1,693,278 27,101 25,861,923 24,168,645

Partial Historical (Nov-Dec)/ Partial Forecast (Jan - Oct) 2018-2019 133,954 13,826,726 8,166,301 4,068,904 1,744,019 24,953 26,220,838 24,476,819

2019-2020 130,429 13,878,289 8,103,070 3,953,012 1,732,024 24,414 26,089,215 24,357,191 2020-2021 127,760 14,112,748 8,133,647 3,988,251 1,730,010 24,414 26,386,820 24,656,810

Forecast, Prior to out 2021-2022 123,820 14,321,764 8,133,022 4,039,608 1,729,142 24,414 26,642,629 24,913,487 of model adjustments (losses, unbilled) 2022-2023 118,617 14,528,129 8,117,883 4,001,670 1,728,768 24,414 26,790,714 25,061,946

2023-2024 113,289 14,731,462 8,104,951 3,975,898 1,728,606 24,414 26,950,014 25,221,408



CE Transportation

Company Use

Total Sendout

Total Planning Load

2013-2014 N/A N/A N/A N/A N/A N/A N/A N/A2014-2015 -13.52% 1.71% 8.60% -15.02% -24.97% 63.40% 0.55% 3.49%

Historical Actual 2015-2016 -5.99% 0.01% -2.09% 0.63% -5.33% 9.65% -0.59% -0.19%2016-2017 0.97% 3.12% -7.44% 24.85% -0.99% -20.10% 3.24% 3.57%2017-2018 -3.04% 5.43% 14.19% -7.73% -2.09% 10.97% 5.31% 5.87%

Partial Historical (Nov-Dec)/ Partial Forecast (Jan - Oct) 2018-2019 -3.12% 3.00% 3.47% -7.11% 3.00% -7.93% 1.39% 1.28%

2019-2020 -2.63% 0.37% -0.77% -2.85% -0.69% -2.16% -0.50% -0.49%2020-2021 -2.05% 1.69% 0.38% 0.89% -0.12% 0.00% 1.14% 1.23%

Forecast 2021-2022 -3.08% 1.48% -0.01% 1.29% -0.05% 0.00% 0.97% 1.04%2022-2023 -4.20% 1.44% -0.19% -0.94% -0.02% 0.00% 0.56% 0.60%2023-2024 -4.49% 1.40% -0.16% -0.64% -0.01% 0.00% 0.59% 0.64%

Historical Average Annual % Growth - 2013-14 to 2017-18 -5.55% 2.55% 2.96% -0.37% -8.91% 12.27% 2.10% 3.16%

Forecast Average Annual % Growth - 2019-20 to 2023-24 -3.46% 1.50% 0.01% 0.14% -0.05% 0.00% 0.81% 0.88%



Page A-353

Lawrence





2013-2014 3,040 40,806 2,867 749 47,462 2014-2015 2,821 41,439 2,906 708 47,875

Historical Actual 2015-2016 2,636 42,178 2,981 688 48,484 2016-2017 2,562 42,749 2,923 788 49,022

Partial Historical (Nov-Jun)/ Partial Forecast (Jul - Oct) 2017-2018 2,497 43,258 2,970 774 49,499

2018-2019 2,427 43,624 3,017 767 49,834 2019-2020 2,357 44,037 3,047 771 50,211 2020-2021 2,288 44,436 3,058 774 50,556

Forecast 2021-2022 2,220 44,847 3,059 776 50,901 2022-2023 2,151 45,232 3,056 778 51,218 2023-2024 2,083 45,592 3,057 782 51,513



2013-2014 N/A N/A N/A N/A N/A2014-2015 -7.21% 1.55% 1.38% -5.44% 0.87%

Historical Actual 2015-2016 -6.58% 1.78% 2.58% -2.82% 1.27%2016-2017 -2.81% 1.35% -1.95% 14.53% 1.11%

Partial Historical (Nov-Jun)/ Partial Forecast (Jul - Oct) 2017-2018 -2.50% 1.19% 1.59% -1.80% 0.97%

2018-2019 -5.27% 2.05% 3.21% -2.67% 1.66%2019-2020 -5.62% 1.80% 2.59% -0.42% 1.44%2020-2021 -2.92% 0.91% 0.37% 0.38% 0.69%

Forecast 2021-2022 -3.00% 0.92% 0.03% 0.30% 0.68%2022-2023 -3.09% 0.86% -0.09% 0.32% 0.62%2023-2024 -3.19% 0.79% 0.03% 0.43% 0.58%

Historical Average Annual % Growth - 2013-14 to 2016-17 -5.55% 1.56% 0.65% 1.72% 1.08%Forecast Average Annual % Growth - 2018-19 to 2023-24 -3.01% 0.89% 0.26% 0.38% 0.66%



Page A-354





CE Transportation


Total Planning Load

2013-2014 67,233 4,253,947 2,219,727 1,925,973 1,612,100 38,089 8,504,970 6,892,870 2014-2015 56,444 4,306,812 2,310,239 1,782,751 1,373,792 37,824 8,494,071 7,120,279

Historical Actual 2015-2016 51,710 4,293,659 2,410,004 1,536,242 1,228,958 34,442 8,326,057 7,097,099 2016-2017 50,018 4,416,940 2,184,234 1,560,059 921,892 33,569 8,244,820 7,322,928

Partial Historical (Nov-Jun)/ Partial Forecast (Jul - Oct) 2017-2018 47,402 4,500,324 2,279,567 1,297,935 821,227 34,325 8,159,553 7,338,326

2018-2019 48,675 4,725,067 2,376,019 1,401,537 945,919 29,608 8,580,906 7,634,986 2019-2020 47,015 4,644,206 2,371,531 1,466,553 927,763 32,536 8,561,841 7,634,079 2020-2021 45,701 4,682,280 2,395,417 1,519,339 927,763 32,536 8,675,274 7,747,511

Forecast, Prior to out 2021-2022 44,384 4,715,477 2,390,210 1,560,413 927,763 32,536 8,743,020 7,815,258 of model adjustments (losses, unbilled) 2022-2023 42,952 4,752,519 2,378,689 1,587,215 927,763 32,536 8,793,910 7,866,148

2023-2024 41,443 4,791,470 2,367,036 1,606,457 927,763 32,536 8,838,942 7,911,179



CE Transportation

Company Use

Total Sendout

Total Planning Load

2013-2014 N/A N/A N/A N/A N/A N/A N/A N/A2014-2015 -16.05% 1.24% 4.08% -7.44% -14.78% -0.70% -0.13% 3.30%

Historical Actual 2015-2016 -8.39% -0.31% 4.32% -13.83% -10.54% -8.94% -1.98% -0.33%2016-2017 -3.27% 2.87% -9.37% 1.55% -24.99% -2.53% -0.98% 3.18%

Partial Historical (Nov-Dec)/ Partial Forecast (Jan - Oct) 2017-2018 -5.23% 1.89% 4.36% -16.80% -10.92% 2.25% -1.03% 0.21%

2018-2019 2.69% 4.99% 4.23% 7.98% 15.18% -13.74% 5.16% 4.04%2019-2020 -3.41% -1.71% -0.19% 4.64% -1.92% 9.89% -0.22% -0.01%2020-2021 -2.80% 0.82% 1.01% 3.60% 0.00% 0.00% 1.32% 1.49%

Forecast 2021-2022 -2.88% 0.71% -0.22% 2.70% 0.00% 0.00% 0.78% 0.87%2022-2023 -3.23% 0.79% -0.48% 1.72% 0.00% 0.00% 0.58% 0.65%2023-2024 -3.52% 0.82% -0.49% 1.21% 0.00% 0.00% 0.51% 0.57%

Historical Average Annual % Growth - 2013-14 to 2016-17 -9.39% 1.26% -0.54% -6.78% -17.00% -4.12% -1.03% 2.04%

Forecast Average Annual % Growth - 2018-19 to 2023-24 -3.17% 0.28% -0.08% 2.77% -0.39% 1.90% 0.59% 0.71%



Page A-355

Springfield





2013-2014 10,744 83,530 7,916 1,811 104,001 2014-2015 10,174 85,010 8,087 1,667 104,937

Historical Actual 2015-2016 9,779 86,005 8,143 1,649 105,575 2016-2017 9,603 86,986 8,003 1,870 106,461 2017-2018 9,399 87,854 8,106 1,809 107,168

Partial Historical (Nov-Dec)/ Partial Forecast (Jan - Oct) 2018-2019 9,135 89,244 8,152 1,720 108,250

2019-2020 8,887 90,580 8,196 1,722 109,386 2020-2021 8,652 91,068 8,170 1,713 109,604

Forecast 2021-2022 8,410 91,953 8,173 1,707 110,243 2022-2023 8,172 93,529 8,163 1,702 111,565 2023-2024 7,933 94,601 8,144 1,699 112,376



2013-2014 N/A N/A N/A N/A N/A2014-2015 -5.30% 1.77% 2.15% -7.95% 0.90%

Historical Actual 2015-2016 -3.89% 1.17% 0.70% -1.07% 0.61%2016-2017 -1.79% 1.14% -1.73% 13.40% 0.84%2017-2018 -2.13% 1.00% 1.29% -3.27% 0.66%

Partial Historical (Nov-Dec)/ Partial Forecast (Jan - Oct) 2018-2019 -2.81% 1.58% 0.56% -4.94% 1.01%

2019-2020 -2.71% 1.50% 0.55% 0.12% 1.05%2020-2021 -2.65% 0.54% -0.32% -0.50% 0.20%

Forecast 2021-2022 -2.80% 0.97% 0.04% -0.37% 0.58%2022-2023 -2.84% 1.71% -0.13% -0.26% 1.20%2023-2024 -2.92% 1.15% -0.24% -0.19% 0.73%

Historical Average Annual % Growth - 2013-14 to 2017-18 -3.29% 1.27% 0.59% -0.03% 0.75%Forecast Average Annual % Growth - 2019-20 to 2023-24 -2.80% 1.09% -0.16% -0.33% 0.68%



Page A-356





CE Transportation


Total Planning Load

2013-2014 67,233 4,253,947 2,219,727 1,925,973 1,612,100 38,089 8,504,970 6,892,870 2014-2015 56,444 4,306,812 2,310,239 1,782,751 1,373,792 37,824 8,494,071 7,120,279

Historical Actual 2015-2016 51,710 4,293,659 2,410,004 1,536,242 1,228,958 34,442 8,326,057 7,097,099 2016-2017 50,018 4,416,940 2,184,234 1,560,059 921,892 33,569 8,244,820 7,322,928 2017-2018 47,402 4,500,324 2,279,567 1,297,935 821,227 34,325 8,159,553 7,338,326

Partial Historical (Nov-Dec)/ Partial Forecast (Jan - Oct) 2018-2019 48,675 4,725,067 2,376,019 1,401,537 945,919 29,608 8,580,906 7,634,986

2019-2020 47,015 4,644,206 2,371,531 1,466,553 927,763 32,536 8,561,841 7,634,079 2020-2021 45,701 4,682,280 2,395,417 1,519,339 927,763 32,536 8,675,274 7,747,511

Forecast, Prior to out 2021-2022 44,384 4,715,477 2,390,210 1,560,413 927,763 32,536 8,743,020 7,815,258 of model adjustments (losses, unbilled) 2022-2023 42,952 4,752,519 2,378,689 1,587,215 927,763 32,536 8,793,910 7,866,148

2023-2024 41,443 4,791,470 2,367,036 1,606,457 927,763 32,536 8,838,942 7,911,179



CE Transportation

Company Use

Total Sendout

Total Planning Load

2013-2014 N/A N/A N/A N/A N/A N/A N/A N/A2014-2015 -9.40% 3.35% 3.69% -10.59% -13.43% -32.86% 0.07% 3.01%

Historical Actual 2015-2016 -9.97% -5.20% -5.20% 39.29% 34.32% -22.40% 3.62% -1.99%2016-2017 1.41% 2.55% 4.20% 6.67% 6.75% -0.94% 4.12% 3.46%2017-2018 -2.16% 2.68% 3.91% 0.42% 4.91% -4.58% 2.36% 1.70%

Partial Historical (Nov-Dec)/ Partial Forecast (Jan - Oct) 2018-2019 -1.50% 5.05% 0.94% -3.35% -2.19% 63.06% 1.56% 2.56%

2019-2020 -3.98% 0.07% -2.95% 2.97% -2.04% 0.47% -0.11% 0.39%2020-2021 -2.83% 0.52% -0.27% 0.46% 0.00% 0.00% 0.25% 0.31%

Forecast 2021-2022 -3.03% 0.61% 0.16% 1.47% 0.00% 0.00% 0.68% 0.84%2022-2023 -3.09% 1.47% -0.30% 0.81% 0.00% 0.00% 0.75% 0.93%2023-2024 -3.17% 0.97% -0.45% 1.44% 0.00% 0.00% 0.67% 0.83%

Historical Average Annual % Growth - 2013-14 to 2017-18 -5.15% 0.78% 1.57% 7.47% 6.82% -16.23% 2.53% 1.52%

Forecast Average Annual % Growth - 2019-20 to 2023-24 -3.03% 0.89% -0.22% 1.04% 0.00% 0.00% 0.58% 0.73%



Page A-357

Total





2013-2014 23,022 251,846 24,639 5,343 304,850 2014-2015 21,653 256,641 25,094 5,002 308,390

Historical Actual 2015-2016 20,751 260,848 25,442 4,946 311,987 2016-2017 20,301 264,696 25,014 5,694 315,705

Partial Historical (Nov-Jun)/ Partial Forecast (Jul - Oct) [Lawrence] 2017-2018 19,780 268,895 25,429 5,508 319,611

Partial Historical (Nov-Dec)/ Partial Forecast (Jan - Oct) [Springfield and Brockton] 2018-2019 19,200 272,812 25,751 5,129 322,891

2019-2020 18,629 276,620 25,929 5,141 326,319 2020-2021 18,072 279,466 25,955 5,131 328,624

Forecast 2021-2022 17,508 282,694 25,959 5,123 331,284 2022-2023 16,948 286,603 25,925 5,118 334,594 2023-2024 16,387 289,974 25,894 5,114 337,368



2013-2014 N/A N/A N/A N/A N/A2014-2015 -5.95% 1.90% 1.84% -6.37% 1.16%

Historical Actual 2015-2016 -4.17% 1.64% 1.39% -1.12% 1.17%2016-2017 -2.17% 1.48% -1.68% 15.12% 1.19%

Partial Historical (Nov-Jun)/ Partial Forecast (Jul - Oct) [Lawrence] 2017-2018 -2.57% 1.59% 1.66% -3.28% 1.24%Partial Historical (Nov-Dec)/ Partial Forecast (Jan - Oct) [Springfield and Brockton] 2018-2019 -2.93% 1.46% 1.27% -6.87% 1.03%

2019-2020 -2.97% 1.40% 0.69% 0.22% 1.06%2020-2021 -2.99% 1.03% 0.10% -0.19% 0.71%

Forecast 2021-2022 -3.12% 1.16% 0.02% -0.15% 0.81%2022-2023 -3.20% 1.38% -0.13% -0.09% 1.00%2023-2024 -3.31% 1.18% -0.12% -0.08% 0.83%

Historical Average Annual % Growth - 2013-14 to 2017-18 -3.72% 1.65% 0.79% 0.76% 1.19%

Forecast Average Annual % Growth - 2019-20 to 2023-24 -3.15% 1.19% -0.03% -0.13% 0.84%



Page A-358





CE Transportation


Total Planning Load

2013-2014 467,166 24,148,423 14,481,891 10,203,308 7,127,010 95,233 49,396,022 42,269,012 2014-2015 411,616 24,669,071 15,369,639 8,986,390 5,864,402 92,614 49,529,331 43,664,928

Historical Actual 2015-2016 377,444 24,240,630 15,027,264 10,109,582 6,529,048 85,894 49,840,814 43,311,765 2016-2017 379,719 24,943,409 14,462,565 11,396,310 6,444,500 78,683 51,260,686 44,816,186

Partial Historical (Nov-Jun)/ Partial Forecast (Jul - Oct) [Lawrence] 2017-2018 368,734 25,926,741 15,748,617 10,788,854 6,493,979 81,171 52,914,117 46,420,138 Partial Historical (Nov-Dec)/ Partial Forecast (Jan - Oct) [Springfield and Brockton] 2018-2019 362,946 26,958,381 16,171,313 10,409,997 6,582,372 86,755 53,989,393 47,407,020

2019-2020 350,591 26,934,603 15,937,741 10,505,981 6,472,715 89,296 53,818,211 47,345,497 2020-2021 341,701 27,250,792 15,977,347 10,617,598 6,470,701 89,296 54,276,734 47,806,033

Forecast, Prior to out 2021-2022 331,349 27,544,329 15,980,206 10,784,965 6,469,833 89,296 54,730,146 48,260,313 of model adjustments (losses, unbilled) 2022-2023 319,670 27,912,524 15,937,123 10,815,630 6,469,458 89,296 55,074,243 48,604,785

2023-2024 307,821 28,238,837 15,888,184 10,884,248 6,469,297 89,296 55,408,386 48,939,089



CE Transportation

Company Use

Total Sendout

Total Planning Load

2013-2014 N/A N/A N/A N/A N/A N/A N/A N/A2014-2015 -11.89% 2.16% 6.13% -11.93% -17.72% -2.75% 0.27% 3.30%

Historical Actual 2015-2016 -8.30% -1.74% -2.23% 12.50% 11.33% -7.26% 0.63% -0.81%2016-2017 0.60% 2.90% -3.76% 12.73% -1.29% -8.40% 2.85% 3.47%

Partial Historical (Nov-Jun)/ Partial Forecast (Jul - Oct) [Lawrence] 2017-2018 -2.89% 3.94% 8.89% -5.33% 0.77% 3.16% 3.23% 3.58%

Partial Historical (Nov-Dec)/ Partial Forecast (Jan - Oct) [Springfield and Brockton] 2018-2019 -1.57% 3.98% 2.68% -3.51% 1.36% 6.88% 2.03% 2.13%

2019-2020 -3.40% -0.09% -1.44% 0.92% -1.67% 2.93% -0.32% -0.13%2020-2021 -2.54% 1.17% 0.25% 1.06% -0.03% 0.00% 0.85% 0.97%

Forecast 2021-2022 -3.03% 1.08% 0.02% 1.58% -0.01% 0.00% 0.84% 0.95%2022-2023 -3.52% 1.34% -0.27% 0.28% -0.01% 0.00% 0.63% 0.71%2023-2024 -3.71% 1.17% -0.31% 0.63% 0.00% 0.00% 0.61% 0.69%

Historical Average Annual % Growth - 2013-14 to 2017-18 -5.74% 1.79% 2.12% 1.40% -2.30% -3.92% 1.73% 2.37%

Forecast Average Annual % Growth - 2019-20 to 2023-24 -3.20% 1.19% -0.08% 0.89% -0.01% 0.00% 0.73% 0.83%



Page A-359

Appendix 13 – Daily Planning Load

A separate daily Planning Load forecast was developed for the Brockton, Lawrence, and Springfield Divisions to provide daily results under various weather scenarios for supply planning purposes. The Company’s daily gate-station meter data by division is the source for the daily Planning Load model dependent variable data. Similar to the Customer Segment forecasts, regression modeling was used to develop the daily Planning Load models that includes independent variables such as weather and date/season-related variables.

1) Data Descriptions

The following data and variable categories were utilized in the development of the daily Planning Load models:

a) Daily Planning Load Data (Dependent Variable)

Historical Daily Planning Load by division was calculated by subtracting historical daily capacity exempt transportation, special contract, and interruptible loads from historical Total System Sendout. Historical daily Total System Sendout data by division was calculated by summing daily gate station meter reads and daily LNG and propane production. Daily Planning Load data for April 2018 through March 2019 was used in model development for Brockton and Springfield. Daily Planning Load for September 2017 through August 2018 was used for Lawrence. In the 12 months of daily data that was used to develop the Daily Planning Load models, the average daily Planning Load was 69,532 Dth for Brockton, 21,445 Dth for Lawrence and 41,879 Dth for Springfield. Additionally, the highest Planning load day was January 21, 2019 where daily demand was 245,073 Dth in Brockton (weather was 70 EDD, which occurred after a day with 59 EDD) and 145,491 Dth in Springfield (weather was 73 EDD, which occurred after a day with 66 EDD). The highest Planning Load Day in Lawrence’s data set occurred on January 6, 2018 where daily demand was 72,889 Dth (weather was 75 EDD, which occurred after a day with 73 EDD).

b) Weather Data

The daily EDD data by division was used to measure weather. The following variations of the EDD variable were tested in preliminary daily Planning Load models to test the responsiveness of daily Planning Load to nonlinear forms of EDD data, simple EDD transformations and prior day EDDs:

• EDD • EDDs base 45 (positive values of EDD – 20) • Prior Day EDD • Second Prior Day EDD



Page A-360

c) Date/Season-Related Variables

Daily Planning Load is also likely to be responsive to time-related elements (e.g., the day of the week or the season); dummy variables were created to represent specific time periods throughout the year.1 For example, dummy variables were created for the months of the year and workdays.

d) Interaction Variables

Interactions between the weather data and the date/season-related dummy variables were created to reflect possible time-related differences in the relationship between daily Planning Load and other explanatory variables. Interactions are created by multiplying the values of two or more variables. The following are examples of interactions that were tested and the reasoning behind the development of each of these variables:

• January EDDs, February EDDs…: Customer responsiveness to weather may vary by month • Winter month prior day EDDs: Customer responsiveness to prior day weather my vary by season

2) Daily Planning Load Model

The following tables provide the model statistics and parameters for the Brockton, Lawrence, and Springfield daily Planning Load regression equations:

1 The time-responsiveness of daily loads can be explained by considerations such as: (a) many industrial customers operate only on week days, and therefore do not use gas for process loads on weekends; (b) heating loads for commercial buildings that have been unoccupied all weekend may be different on Mondays, while the cool building is being warmed up; and (c) many businesses, schools, etc have different schedules in the summer compared to the remainder of the year.



Page A-361

Daily Planning Load Model Results

Brockton



Page A-362

Lawrence



Page A-363

Springfield

In addition to EDDs, several significant variables indicate that the daily Planning Load for each division changes based on extreme temperatures, seasons, and days of the week. The R2 of 0.9946 for Brockton, 0.9950 for Lawrence, and 0.9938 for Springfield indicates that the equations each explain over 99% of the variation in daily Planning Load.

The daily Planning Load models were used to generate a daily Planning Load shape for the split-year November 2019 through October 2020, for each division assuming normal weather. For each year of the Forecast Period for the Normal Year, the division-specific daily Planning Load shape is adjusted by applying calibration percentages that are calculated so that the sum of the daily forecast loads equals the Normal Year Planning Load forecasts developed using the division-specific Customer Segment models. The calibration percentages and the data used to calculate the Normal Year calibration percentages for each division are shown below.



Page A-364

Customer Segment vs. Daily Model Normal Year Calibration Percentages (%)

Brockton

Lawrence

Springfield

The table below summarizes by division the total, average day and peak day results for the Normal Year Planning Load over the Forecast Period.

Planning Load Model Forecast (Dth-Normal Year)2

2 Excludes demand for Leap Year for 2019/20 and 2023/24 split years

Split Year

Daily Planning Load Model

Results (Dth)

Customer Segment Model Results

(including Losses and Unbilled) (Dth) Difference Calibration (%)

2019/20 24,625,893 24,781,189 155,296 0.6%2020/21 24,625,893 25,086,197 460,304 1.9%2021/22 24,625,893 25,347,401 721,508 2.9%2022/23 24,625,893 25,498,595 872,702 3.5%2023/24 24,625,893 25,660,807 1,034,914 4.2%

Split Year


Results (Dth)

Customer Segment Model Results (including Losses

and Unbilled) (Dth) Difference Calibration (%)2019/20 7,706,488 7,770,257 63,769 0.8%2020/21 7,706,488 7,885,484 178,996 2.3%2021/22 7,706,488 7,954,224 247,736 3.2%2022/23 7,706,488 8,005,872 299,384 3.9%2023/24 7,706,488 8,051,601 345,113 4.5%

Split Year


Results (Dth)


and Unbilled) (Dth) DifferenceCalibration

(%)2019/20 15,285,860 15,640,048 354,187 2.3%2020/21 15,285,860 15,688,724 402,864 2.6%2021/22 15,285,860 15,820,341 534,481 3.5%2022/23 15,285,860 15,967,659 681,799 4.5%2023/24 15,285,860 16,099,411 813,550 5.3%

Demand Average Day Peak Day Demand Average Day Peak Day Demand Average Day Peak Day2019/20 24,781,189 67,894 214,950 7,770,257 21,288 64,669 15,640,048 42,849 129,612 2020/21 25,086,197 68,729 217,596 7,885,484 21,604 65,628 15,688,724 42,983 130,016 2021/22 25,347,401 69,445 219,861 7,954,224 21,792 66,200 15,820,341 43,343 131,107 2022/23 25,498,595 69,859 221,173 8,005,872 21,934 66,630 15,967,659 43,747 132,327 2023/24 25,660,807 70,304 222,580 8,051,601 22,059 67,010 16,099,411 44,108 133,419

CAGR 0.70% 0.70% 0.70% 0.71% 0.71% 0.71% 0.58% 0.58% 0.58%

Brockton Lawrence SpringfieldSplit Year



Page A-365

Appendix 14: Design Year and Design Day Planning Load Results

Design Year Planning Load

The methodology to produce design year planning load by division is the same at the methodology to produce normal year planning load for each division, except the normal year forecasted EDDs are replaced with design year forecasted EDDs. The step-by-step methodology that was used for the Brockton, Springfield, and Lawrence divisions to calculate design year planning load is outlined below:

1. The quarterly UPC models for each customer segment were applied to forecasted design year EDD to develop design year UPC forecasts. The design year UPC forecasts were multiplied by the customer count forecasts for each customer segment to produce design year demand for each customer segment. The sum of the customer segment design year demand was adjusted for company use, unbilled, and losses to produce total design year firm sendout. The tables below illustrate these calculations by division:

Design Year UPC1

(from running UPC models with Forecast Design Year EDD)

Brockton

Lawrence

1 Split Year refers to calendar Q4 to Q3

Split Year

Residential Non-Heat

UPCResidential

Heat UPCC&I LLF

UPC C&I HLF UPC2019/20 18.61 110.99 625.40 1,542.64 2020/21 18.85 111.29 625.73 1,556.35 2021/22 18.95 111.43 625.54 1,580.19 2022/23 18.86 111.53 625.24 1,568.62 2023/24 18.74 111.62 624.87 1,559.84

Split YearResidential

Non-Heat UPCResidential

Heat UPCC&I LLF

UPCC&I HLF

UPC2019/20 20.92 118.77 883.59 1,978.43 2020/21 20.95 118.78 888.87 2,039.87 2021/22 20.97 118.69 886.87 2,088.60 2022/23 20.95 118.61 883.89 2,117.66 2023/24 20.88 118.54 880.08 2,134.11



Page A-366

Springfield

Customer Counts2

Brockton

Lawrence

Springfield

2 Split Year refers to calendar Q4 to Q3

Split Year


UPCResidential

Heat UPCC&I LLF

UPCC&I HLF

UPC2019/20 20.09 103.85 757.75 2,997.09 2020/21 20.05 103.83 756.82 3,032.21 2021/22 20.00 103.59 756.24 3,086.38 2022/23 19.95 103.35 755.33 3,118.10 2023/24 19.90 103.14 754.17 3,167.86

Split YearResidential Non-Heat


2019/20 7,384.38 142,003.35 14,686.32 2,648.13 2020/21 7,131.26 143,961.08 14,726.83 2,643.77 2021/22 6,878.14 145,894.60 14,726.88 2,640.08 2022/23 6,625.01 147,842.46 14,705.47 2,637.20 2023/24 6,371.89 149,780.91 14,692.91 2,633.00



2019/20 2,357.14 44,036.82 3,046.61 770.63 2020/21 2,288.35 44,436.20 3,058.01 773.58 2021/22 2,219.73 44,846.80 3,059.03 775.87 2022/23 2,151.14 45,232.36 3,056.19 778.36 2023/24 2,082.55 45,591.94 3,056.96 781.68



2019/20 8,887.37 90,580.12 8,196.49 1,721.75 2020/21 8,652.30 91,068.49 8,170.50 1,713.16 2021/22 8,410.33 91,952.60 8,173.47 1,706.75 2022/23 8,171.56 93,528.53 8,163.06 1,702.29 2023/24 7,932.68 94,601.00 8,143.82 1,698.98



Page A-367

Design Year Demand (Design Year UPC * Customer Count)3

Brockton

Lawrence

Springfield

Design Year Adjustments: Company Use, Unbilled, Losses

Brockton

3 Includes adjustments to convert calendar quarters to gas year; split year is November-October



2019/20 137,292 15,798,597 9,252,793 4,085,417 2020/21 134,387 16,059,857 9,287,766 4,120,291 2021/22 130,212 16,295,007 9,287,868 4,171,452 2022/23 124,774 16,527,583 9,271,428 4,133,371 2023/24 119,210 16,757,010 9,257,558 4,107,393



2019/20 49,261 5,261,073 2,725,948 1,529,937 2020/21 47,881 5,303,568 2,751,308 1,582,972 2021/22 46,499 5,340,913 2,746,287 1,624,234 2022/23 45,002 5,383,422 2,734,441 1,651,235 2023/24 43,427 5,428,517 2,722,806 1,670,744



2019/20 178,386 9,441,561 6,260,405 5,173,909 2020/21 173,347 9,490,791 6,226,544 5,197,064 2021/22 168,111 9,550,663 6,237,735 5,271,661 2022/23 162,926 9,693,739 6,220,508 5,313,234 2023/24 157,775 9,790,662 6,193,937 5,388,208

Split Year Adjustments2019/20 518,071 2020/21 523,601 2021/22 528,356 2022/23 531,265 2023/24 534,365



Page A-368

Lawrence

Springfield

Design Year Firm Sendout (Design Year Demand + Design Year Adjustments)

2. The quarterly capacity exempt load models were applied to forecasted design year EDD to develop design year demand forecasts for capacity exempt load. The design year capacity exempt load was adjusted for losses and unbilled and subtracted from the design year firm sendout to produce design year planning load, as illustrated by division in the tables below.

Design Year Capacity Exempt

Brockton



Split Year Brockton Lawrence Springfield2019/20 29,792,170 9,760,485 21,441,899 2020/21 30,125,901 9,882,011 21,475,948 2021/22 30,412,895 9,955,432 21,618,738 2022/23 30,588,421 10,012,543 21,783,709 2023/24 30,775,536 10,064,804 21,926,245

Split Year Capacity Exempt2019/20 1,861,501 2020/21 1,859,487 2021/22 1,858,619 2022/23 1,858,244 2023/24 1,858,083



Page A-369

Lawrence

Springfield

Design Year Adjustments for Capacity Exempt: Losses, Unbilled

Brockton

Lawrence

Split Year Capacity Exempt2019/20 991,787 2020/21 991,787 2021/22 991,787 2022/23 991,787 2023/24 991,787

Split Year Capacity Exempt2019/20 3,982,108 2020/21 3,982,108 2021/22 3,982,108 2022/23 3,982,108 2023/24 3,982,108

Split Year Adjustments2019/20 1,557 2020/21 879 2021/22 495 2022/23 38 2023/24 (76)




Page A-370

Springfield

Design Year Firm Planning Load (Design Year Firm Sendout minus Design Year Capacity Exempt)

3. The daily planning load model described in Appendix 13 was applied to forecasted design year

EDD to develop a design year planning load shape. 4. For each year of the forecast period, the daily design year Planning Load shape was adjusted by

applying calibration percentages to ensure the sum of the daily design year planning load shape equals the design year Planning Load forecasts developed using the quarterly Customer Segment Models.

Customer Segment vs. Daily Model Design Year Calibration Percentages (%)

Brockton


Split Year Brockton Lawrence Springfield2019/20 27,929,112 8,764,810 17,447,862 2020/21 28,265,535 8,886,132 17,482,738 2021/22 28,553,782 8,959,309 17,624,722 2022/23 28,730,138 9,016,258 17,789,204 2023/24 28,917,530 9,068,421 17,931,265

Split Year


Results (Dth)


(including Losses and Unbilled) (Dth) Difference Calibration (%)

2019/20 27,865,496 27,929,112 63,617 0.2%2020/21 27,865,496 28,265,535 400,040 1.4%2021/22 27,865,496 28,553,782 688,286 2.5%2022/23 27,865,496 28,730,138 864,642 3.1%2023/24 27,865,496 28,917,530 1,052,034 3.8%



Page A-371

Lawrence

Springfield

Design Day Planning Load The Design Year daily EDD data includes the Design Day value of 78 EDD for Brockton, 78 EDD for Springfield, and 80 EDD for Lawrence; for modeling purposes, Design Day is assumed to occur on January 16, 2020. Therefore, the forecast of demand on design day is included in the daily forecast of design year demand. As explained above, the design year daily EDD values (which includes design day EDD on January 16) were applied to the daily planning load model for Brockton, Lawrence, and Springfield divisions described in Appendix 13 to develop a design year Planning Load shape by division; that shape includes design day Planning Load on January 16. For each year of the forecast period, each day of the daily design year Planning Load shape was adjusted to ensure that the sum of the daily design year planning loads equals the forecasts design year Planning Load developed using the quarterly Customer Segment Models, as shown by division in the tables above. Applying the design year calibration percentages to the January 16 value of the Design Year version of the daily Planning Load shape produces the forecast of design day planning load, as shown by division in the table below:

Design Day Results

Split Year


Results (Dth)


and Unbilled) (Dth) Difference Calibration (%)2019/20 8,543,092 8,764,810 221,718 2.6%2020/21 8,543,092 8,886,132 343,039 4.0%2021/22 8,543,092 8,959,309 416,216 4.9%2022/23 8,543,092 9,016,258 473,166 5.5%2023/24 8,543,092 9,068,421 525,329 6.1%

Split Year


Results (Dth)


(including Losses and Unbilled) (Dth) Difference

Calibration (%)

2019/20 16,905,190 17,447,862 542,672 3.2%2020/21 16,905,190 17,482,738 577,548 3.4%2021/22 16,905,190 17,624,722 719,532 4.3%2022/23 16,905,190 17,789,204 884,014 5.2%2023/24 16,905,190 17,931,265 1,026,075 6.1%

Brockton Lawrence SpringfieldPlanning Load - Design Year 274,137 80,540 155,109

Date2019/20 274,763 82,631 160,088 16-Jan2020/21 278,072 83,774 160,408 16-Jan2021/22 280,908 84,464 161,711 16-Jan2022/23 282,643 85,001 163,220 16-Jan2023/24 284,487 85,493 164,524 16-Jan

Calibrated Daily Model Results



Page A-372

Appendix 15 (Brockton) - Normal Year Base CaseForecast Calculation and Data Flow

Gas YearTotal Planning Load

Total CE Demand CE Demand

Total Firm Demand

Unbilled and Losses Company Use

Residential Heat


C&I Low-Load Factor

C&I High-Load Factor

2010/11 21,466,381 2,746,116 2,747,325 24,212,496 332,387 26,476 12,286,863 189,105 7,198,018 4,178,438 2011/12 17,770,784 2,365,738 2,381,319 20,136,522 22,474 27,636 10,206,353 178,345 5,917,794 3,768,339 2012/13 21,235,476 2,548,808 2,518,690 23,784,285 633,377 20,339 11,904,247 181,212 6,723,465 4,351,763 2013/14 23,911,422 2,556,423 2,556,419 26,467,845 440,572 17,059 13,469,380 180,251 7,789,589 4,570,998 2014/15 25,035,499 1,945,999 1,942,586 26,981,498 586,665 27,874 13,788,704 156,406 8,436,558 3,988,704 2015/16 20,896,257 1,681,234 1,674,222 22,577,491 344,086 30,565 11,384,093 137,048 6,858,803 3,829,908 2016/17 22,644,845 1,692,866 1,711,673 24,337,712 212,481 24,422 12,515,188 141,759 6,797,007 4,628,048 2017/18 25,128,221 1,728,954 1,692,938 26,857,175 1,084,692 27,101 13,420,629 138,411 7,855,767 4,366,591 2018/19 24,982,552 1,765,610 1,744,019 26,748,162 548,914 24,953 13,826,726 133,954 8,166,301 4,068,904 2019/20 24,781,189 1,747,608 1,732,024 26,528,797 455,166 24,414 13,878,289 130,429 8,103,070 3,953,012 2020/21 25,086,197 1,745,220 1,730,010 26,831,417 459,807 24,414 14,112,748 127,760 8,133,647 3,988,251 2021/22 25,347,401 1,744,135 1,729,142 27,091,536 463,900 24,414 14,321,764 123,820 8,133,022 4,039,608 2022/23 25,498,595 1,743,521 1,728,768 27,242,116 466,155 24,414 14,528,129 118,617 8,117,883 4,001,670 2023/24 25,660,807 1,743,294 1,728,606 27,404,100 468,773 24,414 14,731,462 113,289 8,104,951 3,975,898



Page A-373

Appendix 15 (Lawrence) - Normal Year Base CaseForecast Calculation and Data Flow



Total Firm Demand

Unbilled and Losses

Company Use

Residential Heat


C&I Low-Load Factor


2010/11 7,154,525 1,710,569 1,718,774 8,865,094 75,667 30,183 4,335,045 75,606 2,414,145 1,926,243 2011/12 6,064,171 1,501,983 1,500,294 7,566,154 68,114 28,463 3,649,668 68,330 1,999,797 1,753,471 2012/13 6,894,884 1,630,780 1,618,350 8,525,663 204,257 30,587 4,181,705 76,163 2,145,068 1,900,313 2013/14 7,819,594 1,662,849 1,661,360 9,482,443 170,274 38,089 4,730,311 69,650 2,497,129 1,978,479 2014/15 8,067,706 1,428,164 1,423,067 9,495,870 190,935 37,824 4,793,113 58,656 2,583,919 1,836,519 2015/16 6,663,506 1,193,366 1,192,065 7,856,872 82,414 34,442 3,975,668 50,460 2,199,299 1,515,891 2016/17 7,276,374 910,525 913,652 8,186,899 42,606 33,569 4,350,962 49,823 2,146,576 1,560,237 2017/18 7,918,352 908,280 874,055 8,826,632 456,292 34,325 4,616,311 49,513 2,329,147 1,375,270 2018/19 7,790,838 956,192 945,919 8,747,030 176,397 29,608 4,725,067 48,675 2,376,019 1,401,537 2019/20 7,770,257 935,844 927,763 8,706,101 152,342 32,536 4,644,206 47,015 2,371,531 1,466,553 2020/21 7,885,484 935,961 927,763 8,821,445 154,370 32,536 4,682,280 45,701 2,395,417 1,519,339 2021/22 7,954,224 936,109 927,763 8,890,333 155,659 32,536 4,715,477 44,384 2,390,210 1,560,413 2022/23 8,005,872 936,209 927,763 8,942,081 156,616 32,536 4,752,519 42,952 2,378,689 1,587,215 2023/24 8,051,601 936,270 927,763 8,987,871 157,436 32,536 4,791,470 41,443 2,367,036 1,606,457



Page A-374

Appendix 15 (Springfield) - Normal Year Base CaseForecast Calculation and Data Flow



Total Firm Demand

Unbilled and Losses

Company Use

Residential Heat


C&I Low-Load Factor


2010/11 14,137,770 2,894,930 2,879,087 17,032,700 314,139 41,188 7,525,668 266,084 5,212,867 3,688,597 2011/12 11,716,472 2,744,780 2,762,795 14,461,251 (3,762) 36,086 6,239,971 244,508 4,589,294 3,337,138 2012/13 13,559,347 3,009,614 2,990,148 16,568,962 394,920 37,465 7,296,162 260,256 4,661,255 3,938,370 2013/14 15,306,484 3,150,174 3,156,526 18,456,657 283,934 40,085 8,377,730 229,788 5,627,480 3,891,288 2014/15 15,770,773 2,715,893 2,715,069 18,486,666 395,036 26,915 8,522,206 208,464 5,867,195 3,467,674 2015/16 13,488,928 3,456,703 3,422,586 16,945,630 395,144 20,887 6,903,252 179,958 4,762,051 4,718,454 2016/17 14,440,023 3,724,495 3,753,156 18,164,518 201,904 20,691 7,561,317 184,737 5,093,630 5,073,577 2017/18 15,627,326 4,077,436 3,999,237 19,704,762 739,894 19,744 8,104,974 182,981 5,600,781 5,134,586 2018/19 15,591,608 3,931,722 3,892,434 19,523,330 374,970 32,194 8,406,588 180,317 5,628,994 4,939,556 2019/20 15,640,048 3,850,058 3,812,928 19,490,106 360,081 32,346 8,412,107 173,146 5,463,140 5,086,416 2020/21 15,688,724 3,849,668 3,812,928 19,538,392 360,491 32,346 8,455,764 168,240 5,448,284 5,110,008 2021/22 15,820,341 3,850,094 3,812,928 19,670,435 363,105 32,346 8,507,088 163,145 5,456,974 5,184,944 2022/23 15,967,659 3,850,345 3,812,928 19,818,003 365,801 32,346 8,631,876 158,101 5,440,551 5,226,746 2023/24 16,099,411 3,850,590 3,812,928 19,950,001 368,234 32,346 8,715,904 153,089 5,416,196 5,301,893



Page A-375

Appendix 15 (Total) - Normal Year Base CaseForecast Calculation and Data Flow



Total Firm Demand

Unbilled and Losses Company Use

Residential Heat


C&I Low-Load Factor


2010/11 42,758,676 7,351,615 7,345,186 50,110,291 722,193 97,847 24,147,576 530,795 14,825,030 9,793,278 2011/12 35,551,427 6,612,500 6,644,408 42,163,927 86,826 92,185 20,095,992 491,183 12,506,885 8,858,948 2012/13 41,689,707 7,189,203 7,127,188 48,878,910 1,232,554 88,391 23,382,114 517,631 13,529,788 10,190,446 2013/14 47,037,500 7,369,446 7,374,304 54,406,945 894,780 95,233 26,577,421 479,689 15,914,198 10,440,765 2014/15 48,873,977 6,090,056 6,080,721 54,964,033 1,172,636 92,614 27,104,023 423,526 16,887,672 9,292,897 2015/16 41,048,690 6,331,303 6,288,873 47,379,993 821,644 85,894 22,263,013 367,466 13,820,153 10,064,253 2016/17 44,361,242 6,327,886 6,378,481 50,689,129 456,990 78,683 24,427,467 376,319 14,037,213 11,261,862 2017/18 48,673,899 6,714,670 6,566,230 55,388,569 2,280,878 81,171 26,141,914 370,905 15,785,695 10,876,447 2018/19 48,364,999 6,653,524 6,582,372 55,018,522 1,100,281 86,755 26,958,381 362,946 16,171,313 10,409,997 2019/20 48,191,494 6,533,511 6,472,715 54,725,004 967,589 89,296 26,934,603 350,591 15,937,741 10,505,981 2020/21 48,660,405 6,530,849 6,470,701 55,191,254 974,667 89,296 27,250,792 341,701 15,977,347 10,617,598 2021/22 49,121,966 6,530,338 6,469,833 55,652,304 982,664 89,296 27,544,329 331,349 15,980,206 10,784,965 2022/23 49,472,126 6,530,074 6,469,458 56,002,200 988,572 89,296 27,912,524 319,670 15,937,123 10,815,630 2023/24 49,811,818 6,530,154 6,469,297 56,341,972 994,443 89,296 28,238,837 307,821 15,888,184 10,884,248



Page A-376

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