energy demand analysis and forecasting

8/9/2019 Energy Demand Analysis and Forecasting

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Energy Demand Analysis and

Forecasting(Energy Planning and Management)

Rabin ShresthaVisiting Faculty

Pulchowk Campus, 2010

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Energy Demand Forecasting

It is the general term used to denote the

estimation of some unknown variable in the

future.

All organizations need forecasts forplanning purposes.

Forecasts are necessary inputs to decision

models, and are building blocks foroptimization and simulation models.

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Sectoral Energy Demand

Industry

Agriculture

Residential

Commercial/Institutional

Transportation

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Energy Consumption Sectors in

Nepal

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Source : Energy Sector Synopsis Report 2006, Water and Energy Commission Secretariat


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Energy Sources and Economic Sector

Energy Industry Residential Transport Others

Coal

Oil

Electricity

Solar

Total

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Forecasting Techniques

Time Series Method

Econometric Method End Use Method


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Characteristics of Different Techniques

================================== ===========================

haracteristics Time eries conometric nd Use

--------------------------------------------------------------------------------------------------------

Best forecast Months to a 1 - 10 years 10 - 30 years

horizon few years

Data requirement Minimal 8 -10 years time Proportional to

series desired detail

pecialized skills Trivial for trend elatively easy No specialized

to significant training

uitability for analysis Poor Good for variables Generally the best

of system shocks/scenario explicitly in model method of all====================================== =======================

ource: IA A (1984)


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Time Series Method

Time series method search for systematic and recurrent

relationship between demands at various points in time

Basic principle is energy demand is function of time.

All information required to produce the forecast in

contained in the time series. No need for other

explanatory variables.

The predictive accuracy varies form application to

application


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Time Series Components

Long Term Trend: any relatively consistent rate ofchange from year to year

Cyclical Behavior: a pattern that repeats itself over

many years, generally with a similar frequency and

amplitude

Seasonal Variation: a pattern that repeats itself in

some similar fashion between different periods of the

year

Random variation: Any effect remaining after

adjustment of above effects. Political events, adverse

weather conditions, etc


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Sample Trends


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Guidelines for applying

time series method Avoid using monthly or quarterly data to indicate annual

trends

Be careful in cases where sporadic changes from

period to period are very large relative to averagehistorical load

Adjust data for extraordinary events like wars, strikes,natural disasters, extreme weather conditions

Watch for significant economic events like oil priceshock


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Smoothing Techniques

Moving Average

Exponential Smoothing

Box-Jenkins Analysis


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Moving Average

Yt=(Yt-(M-1)/2 + Yt+1-(M-1)/2 + Yt+2-(M-1)/2 +Yt+ (M-1)/2)/M

Y2 = (Y1 + Y2 + Y3)/3Y3 = (Y2 + Y3 + Y4)/3

Y4 = (Y3 + Y4 + Y5)/3


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Exponential Smoothing

St = Yt + (1- )St-1 0 1

is smoothing constant


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Other Time Series Models

Exponential Smoothing Forecasting Model:

First, second, third -order

Exponentially Weighted Moving Average

Model

Holt-Winters and Box-Jenkins Forecasting

Model

Hybrid Forecasting Model


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Econometric Method

Econometric method are usually more sophisticated and intheory, promise greater forecasting accuracy

The advantages of econometric method is that they can

take into account a number of important demand

determining variables, such as price and income. This approach combines economic theory and statistical

techniques

Past energy demand is first correlated with other variables

such as prices and income. Then future demands arerelated to predicted growth of these other variables.


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Microeconomics: Consumers and

Producers Two key players in the economy: Consumer and

Producer

Consumer tries to Maximize the satisfaction withinthe given expenditure

Producers tries to Minimize total cost of productionwith electricity as one of the inputs

Therefore, need to examine consumer andproducer demand separately


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Energy Demand by Household

Eenergy demand by household at any time

Qe=Qe (Pe, Pnon-e, I, Z)

Qe= I/Pe

Per capita agricultural GDP is used as proxi for

consumer income


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Market Demand Demand for groups of individual consumer

are Market demand

Requires some form of aggregation

assuming similarity of consumers or firmsD = Qe = No of consumers x Qe

= (Population/HH size)x(Coverage) x (Pe, Per

capita GDP)


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Energy Demand of a firm

qe = qe(Pe, Pnon-e, x, S)

Output is measured in terms ofIndustrial

GDP


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Market Demand

Demand for group of firms are Market

demand

Requires some form of aggregation

assuming similarity of firms

Qe = qe = No of firms qe


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Econometric Method

Step 1.Define the likely explanatory variables (GDP,

disposable income, price, etc.)

Step 2. Define the functional relationship of

explanatory variables to electricity demand

Step 3.Research time series of these variables

Step 4.Perform multiple regression analysisStep 5.Test and validate model


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Econometric Method

Residential Demand Driving Variables

Price of Different types ofEnergy

Disposable Income per household

Number of Customers

Appliance Price Index

People per

Household


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Econometric Method

Commercial Demand Driving Variables

Price of

Energy

Occupied Office Space

Commercial Employment

Price of Competing Fuels


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Econometric Method

Industrial Demand Driving Variables

Level ofIndustrial Output

Price of

Energy

Industrial Employment

Output perWorker

Earnings in Manufacturing


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Constant Elasticity Model

The most widely used formulation has the form:

Q=A0 Xa Yb Zc

ln(Q) = ln A0 + a ln(X) + b ln(Y) + c ln(Z)

1

0

!

x

x acbaXZYA

X

Qa

XX

QQ

!

x

x


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Partial Adjustment Model

ln Q(t) = ln A0 + a ln Q(t-1)+ b ln X(t) + c ln Y(t)

This type o model gives the energy usage response

inertial e ects

A consumer may receive a price signal and not respond

immediately, but take 2-3 years


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Limitation of Econometric Method

Past relationship would prevail in future

The relationship between driving variable is

really causal or is it casual (or coincidental) Role of new variable in the future is ignored

Data availability and accuracy


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End-Use Method

The basic concept is forecasting the annual

number of additions of energy consuming

devices and energy consumption per device.

These two quantities gives annual energyconsumption.

Also refer to as

Engineering Method orsimulation method


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Types of End-Use

Sector End-Use

Residential/ Commercial Lighting

Space heating/cooling

Cooking

Electro-mechanical

Industrial Indirect heatDirect heat (high temperature)

Process

Motive power

Off-road industrial vehicle

Lighting

Agriculture Motive power, off-road, process

Transport Passenger travel: car, bus, rail, air,

motorcycle

Freight travel: truck, rail, marine, air


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End-Use Model

Total energy consumption by appliance i in year t:

Esit = Nsit UFsit Psi esit HsitNit = number of appliances of type i in sector s in year t

UFit = utilization factor (ratio of the number of appliances in use to the total

stock) of appliance i in sector s in year tPis = fuel consumption by appliance i in sector s

eit = efficiency improvement factor of appliance i in sector s in year t

Hit = annual operating hours of appliance i in sector s in year t

Total energy consumption by sector s in year t:

Est = EsitTotal energy consumption in year t: Et = Est


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Residential End-Use Model Forecast number of household in service territory

Determine current level of appliance in the area

Forecast new energy consuming devices

Forecast future penetration of appliances Determine electricity usage of existing appliances

Forecast future efficiency improvements in energyusage per appliance

Forecast total energy consumption

Validate the forecast


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Example ofResidential Disaggregated

End-Use Modelational Regional eographical rban/Rural Income Level End-use

Country East

South

West

orth

Central

lain

Hilly

rban

Rural

Lo

edium

High

Others

Heating

Cooking

Lighting


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Commercial End Use Model

Classify building types

Classify end uses

Determine building floor area

Determine commercial end-use penetration rateand annual energy usage per floor area

Forecast future building floor area additions

Forecast future penetration rate and annualenergy end use per floor area

Forecast commercial consumption

Validate the forecast


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The End

energy demand analysis and forecasting

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