monetizing opportunities for smart meter data - …...[ monetizing opportunities for smart meter...

Monetizingopportunities for

smart meter data

An EXL whitepaper

Written by

Vikas KumarAssistant Vice PresidentDecision [email protected]

Praveen AbrahamSenior ManagerDecision Analytics

1 © 2016 ExlService Holdings, Inc.

[ Monetizing opportunities for smart meter data ]

From data scarcity to data profusion, from

the periodic frequency of data collection

to real-time data feeds, from benign

customer engagement with companies

to active customer interest in their energy

consumption behaviour, many factors are

bringing in a revolution to the industry.

This presents an opportunity for the

utility industry to capitalize on a data-

rich environment to enhance customer

experience, improve their operational cost

structure and enhance overall industry

performance.

The Big Data challenge in utilities presents

possibilities to grow the both top line and

bottom line of energy retailers, along with

providing additional value to the customers.

Whether it’s accurate billing or advanced

features enabled by smart metering like

connected homes, there is a clear win for

the customers. According to an estimate1,

connected homes, work, and digital cities

will create a $731.79B market opportunity

for utilities by 2020.

Smart meter data can be monetized by

addressing a few key areas of impact:

• Customer experience improvements

through better customer insights, better

understanding of consumption behavior

and reduced billing challenges and by

providing more control to consumers over

their consumption decisions.

• Financial performance enhanced by

growing the topline through tailored

propositions, improved revenue

The global utilities sector is mobilizing to implement smart meters, with different countries at different points in their implementation. In the UK, the implementation of the government’s 2020 plan for completing the rollout of smart meters to every household is well under way and is gaining momentum. Smart meters have started changing customer expectations and business models for utilities.

1. Frost & Sullivan Report, (2014, April 28) - Connected Living: http://www.frost.com/sublib/display-report.do?id=M94C-01-00-00-00

http://www.frost.com/sublib/display-report.do?id=M94C-01-00-00-00



service levels like accurate and timely

billing would become hygiene factors.

Advanced propositions, flexible tariff plans,

information and communication to support

informed choices will become important to

deliver higher value and help differentiate

in the market place.

Data analytics will be the key to

understanding and meeting customer

expectations through appropriate products

and services. The key to success would

be to provide an actionable, real-time, yet

simplistic view to customers to facilitate

their decisions.

At a broad level, customer experience

related to evolution of smart meters can be

categorized into two parts.

1. There will be a need for a host of

offerings around real-time visualization

and control that help customers

analyze and customize different factors

like tariff plans, consumption behavior,

appliance control, dynamic proposition

selection, participation in demand

management and local generation

assurance, reduced cost of operations

and the innovative use of smart data for

monetization.

• Enhanced governance mechanisms

that support for integrated grid and

improvement in overall efficiency of the

grid/industry.

Monetization for enhanced customer experienceCustomer outlooks will go through an

evolution because of an increase in

expectation from what smart meters can

deliver. Current expectations of basic

Overall gains for utility industryImproved financial performanceEnhance customer experience



2. There will be a need for rewards and

loyalty programs that incentivize good

behavior, promote loyalty and provide

an integrated, consistent experience

comparable across other industries

Real-time visualization and control

Smart meters are expected to transform

customer expectations for accessible

real-time appliance level consumption,

customized recommendations, dynamic

pricing, automated breakdown predictions

and corresponding actions and information

about efficient products in order to

reduce their energy bill by controlling and

managing their consumption behavior.

Appliance control and customization

A significant change brought on by smart

meters will be the increased adoption of

home management solutions facilitating

connected homes. Customers will expect

several benefits from these networked

homes:

• The customers would want to have

the ability to control and customize

power consumption at their homes

remotely using an app installed on their

smartphones. Smart meter data analysis

makes it possible to determine the

resource usage of individual appliances,

lights and HVAC system by analyzing

their data patterns. Alerts or alarms could

be activated on the app if an appliance

is consuming more than average

consumption of similar appliances,

enabling the customer to get the

appliance checked for any malfunctions.

This in turn will help customers to keep

their bills within a desirable limit and save

money through the increased longevity of

their appliances.

Utilities can use this opportunity to

generate new revenue streams by

offering insurance and service products

where appliances are tracked real-time.

Furthermore, sophisticated backend

models like principal component analysis

(PCA) can be deployed to cluster

breakdown types and help engineers

reach the site with the precise equipment

required to fix the issue, resulting in

savings by eliminating the cost of multiple



trips. Also, renewal prices can be refined

significantly by looking at previous

performance data of appliances and

charging a premium for cases where

multiple breakdowns are forecasted.

• Machine learning can be applied to

improve energy efficiency, reliability and

comfort by monitoring operations and

using algorithms to adjust it for external

weather while avoiding any manual

interventions. Configuring smart meters to

the real-time GPS location of a customer

can help activate devices depending on

the estimated arrival time after taking

traffic condition into account. This would

help provide a seamless, cost-effective

experience for customers. A four step

approach for implementing algorithms

can be adopted to reach this goal:

1. Identifying consumption histogram,

2. A three-line algorithm for

understanding the effect of external

temperature on consumption

3. A periodic auto-regression (PAR)

algorithm to extract typical daily

profiles

4. Time series similarity search to find

similar consumers

While the first three algorithms analyze

electricity consumption of each household

in terms of its distribution, temperature

sensitivity and daily patterns; the fourth

algorithm finds similarities among

different consumers for adjustments

and recommendations. This tracking and

model can be extremely useful for utilities

by helping them to accurately forecast

customer consumption.

Clusters of activityprofiles

Corresponding cooling andheating gradients

Temperature timeseries

Select cooling andheating gradients

Select a cluster

Activity load +temperature

dependent load +white noise



Dynamic pricing

The price consumers pay do not reflect

true cost of production, and also do not

incentivize customers to shed during peak

loads. The ability to “shave” peak demand

could allow a utility to reduce built capacity

and save on the cost of generation. Utilities

can pass some of these savings to the

customer through dynamic pricing. Smart

metering promises several avenues for

realistic pricing which can drive beneficial

results.

Time of usage (ToU) pricing institutes a

price schedule for electricity usage, under

which electricity is least expensive when

loads are low and most expensive during

peak hours. ToU is based on the fact that

by altering rates at different times of the

day, providers can incentivize customers

to adjust their loads, either manually

or through home energy management

systems.

Singapore employed a dynamic pricing

pilot on its expanding smart grid in 2012,

with dynamic pricing on 30 minute

intervals. The pilot reduced peak

residential loads by 3.9% and total energy

consumption by 2.4%.

Breakdown prediction and security

By capturing real-time consumption data,

utilities can identify breakdowns at an early

stage. This can be achieved by looking at

the consumption pattern of an appliance,

as well as overall household usage levels

to identify significant changes in trends

from an expected value. While being able

to proactively reach out to the customers

would help save utilities on inbound call

volume, customers would also be able to

save on costs through early intervention

before the damage reaches an irreparable

level.

Smart meters also present an opportunity

for data breaches. The risk of hackers

infiltrating systems can be mitigated

through systematic configurations. Utilities

can effectively detect and eliminate the

risk of tampering through advanced

metering infrastructure. Utilities can

consider providing several enhanced

add-on features along with basic gas and

electricity connections, and open doors for



new revenue sources similar to telecom

and digital service providers. For example,

smart meters can be made to work in

two separate modes: ‘home’ and ‘away’

mode. When a customer is not at home

for a long time, he or she can choose a

few appliances that should continue to run

and key in a password that will make the

meter work in ‘away’ mode. When there is

an unexpected usage or a wrong password

keyed to activate an appliance, the smart

meter can send a text message to the

consumer.

Puerto Rico started the smart meter

roll-out along with installation of a 1GW

renewable energy with an aim to reduce

its dependency on expensive oil-fired grid.

The smart meter pilot, in conjunction with

other measures, has already saved USD

17 million per year in reduced electricity

theft within the first three years. This figure

is expected to rise to USD 50 million when

the rollout is expanded.



Rewards and loyaltyWith real-time data feeds from smart

meters, utilities can roll out several reward

and loyalty programs similar to that offered

by retail chains and airlines. Reward

allocation done in real-time provides an

opportunity to make these programs

extremely engaging and generate a higher

level of participation. These programs can

be broadly classified into two sets. While

first set of offers would correspond to

incentives rolled-out to promote loyalty

while driving an appropriate behavior, the

second set corresponds to collaborative

partnership with other industries to capture

cross-sell opportunities.

Incentive roll-out to promote loyalty and ideal behavior

By integrating behavioural economics,

gamification and loyalty programs, utilities

can rollout multiple loyalty programs and

incentive schemes to keep customers

engaged and alter their consumption

patterns. Advanced marketing mix models

(MMM) can be deployed to estimate and

track the impact of various rollouts and

optimize promotional tactics with respect

to profit. Since these programs will involve

investments from utilities, a test-and-

learn process, where RoI is evaluated for

multiple variants of programs rolled out in

small batches is recommended to decide

on the optimal scheme for a large scale

rollout. The deployment of region based

gamification based on rewarding efficient

household consumption levels during peak

hours can serve as a useful tool to enhance

retention by keeping customers engaged

while curtailing peak loads.

A pilot program initiated by National Grid

in New York and Rhode Island offers

loyalty points to customers based on the

amount of energy saved. Rewards can

be redeemed at Home Depot, Amazon.

com etc. or donated to charities. Since the

launch of the pilot in 2009, National Grid

customers in those states have saved

- $73.7M and 800M kilowatt-hours of

electricity.



Collaborative partnership

In order to fully leverage the advantages

of smart meters, utilities should explore

partnerships with other industries. While a

partnership with a telecom company can

be extremely useful to set up the process

and get devices connected to smart meter,

partnership with other industry players can

help rollout advanced gamification along

with loyalty programs.

Utilities usage data at appliance level is

a useful asset that can provide insights

for customer segmentation and optimal

marketing channel. Hence, it should be

utilized as a potential revenue source

while working with other industries. As

an example discounted IoT (Internet of

Things) connections and no network

charges for accessing smart consumption

on smartphone apps, in lieu of customer

segmentation insights can be considered

as potential collaboration terms with a

telecom player.

Monetization of increased efficiency gainsProviders should aim to make the best

use of the AMI (Advanced Metering

Infrastructure) data to increase efficiencies

at all levels of services and offerings to the

customer. A close examination of different

operational elements in the smart grid will

open avenues for growth in top line and

bottom line for utilities.

Customer segmentation based propositions While the AMI data opens up many

possibilities, a key area where utilities

need to work is improving customer

segmentation based on actual

consumption data from smart meters. With

the roll-out of smart meters, the use of data

mining to generate customer segments

to increase marketing effectiveness

and boost potential RoI can be refined

significantly. Segments generated through

predictive analytics programs can be used

to generate target messages with high

precision.



Further, a traditional customer’s lifetime

value can be fine-tuned to look into

cost to serve and revenue at a far more

granular level (e.g. peak vs. off peak,

hourly, daily etc.) as compared to the

current monthly level approach which

can lead to inaccurate classification

and segmentations. Accurate definition

of customer’s lifetime values can help

prioritize, target and acquire the most

valuable customers first while deprioritizing

the ones who might have a low or negative

lifetime value.

With more accurate segments, several

manual activities can be automated to

improve cost efficiency, performance

levels and accuracy. For example, the

current process to identify customers likely

to experience a breakdown is based the

discretion of call center agents. This can

be replaced with an auto alert system for

specific device failures in a household

that trigger home emergencies. In order

to realize these benefits, utilities should

be prepared to leverage analytics by

developing:

• Accurate load curves for every customer,

thus segmenting them based on:

+ Customer attributes

+ Energy consumption pattern

+ Areas triggering peak load at grid level

+ Areas of energy efficiency

+ Renewable energy usage

• The capability to process data at

extremely high velocities in order to

quickly respond to pricing signals and

identify the set of customers who can be

targeted for energy efficiency programs



Demand response managementWhen a production facility is not used,

it represents less efficient use of capital.

However, utilities need to plan their

capacity to be able to meet the peak

demand with a sufficient buffer to deal

with unanticipated events. Hence, any

opportunity to shed peak demand can

help release significant capital for utilities.

It is estimated that a 5% lowering of peak

electricity demand would result in a 50%

price reduction2 to the end consumers.

The dynamic Time of usage (ToU) pricing,

rewards and incentive mechanisms

covered in earlier sections are some of

the methods which can be deployed for

demand response. Further, several new

technologies are available to automate

the process of demand response. Such

technologies forecast the need for load

shedding, communicate the demand

to participating users, automate load

shedding, and verify compliance with

demand-response programs. Utilities

can automate appliances connected to

its users that can reduce consumption at

times of peak demand by delaying draw

marginally, such as turning up refrigeration

and lowering the temperature of hot

water during peak hours. Such programs

have a considerable scope to reduce

peak demands. Utilities can substantially

cut costs through these schemes, and

then pass on some of these benefits to

participating customer based on megawatt

power.

The Fayetteville Public Works Commission,

the largest municipal electric provider

in the state of North Carolina, has rolled

out demand response/home energy

management service commercially. The

demand response service has installed

smart meters with an integrated gateway

module and a programmable networked

thermostat in homes and small business

premises. Initial results show that

consumers have saved as much as 15-20%

of their overall electricity usage compared

to previous years.

A similar advantage for large scale

customers with smart meters and

generation capacity could be the ability to

2. The Power to Choose - Enhancing Demand Response in Liberalised Electricity Markets Findings of IEA Demand Response Project, Presentation 2003



closely monitor, shift, and balance load in

a way that allows them to trade what they

have saved in an energy market. This will

involve sophisticated energy management

systems, incentives, and a viable trading

market.

Additional opportunities across the utilities sectorRegulatory bodies across countries want

to know how investments in smart meters

are helping improve operational efficiencies

and deliver enhanced levels of customer

service. AMI data and the overall adoption

of smart propositions are expected to drive

the benefits at a sector level by closing

gaps between regulatory bodies and

customers, along with incentivising the use

of alternate energy.

Managing demand with analyticsSmart propositions will facilitate a higher

adoption of distributed energy systems

enabling consumers to generate on-premise

energy that can be fed back into the distri-

bution grid. Distributed energy resources

often use renewable energy (RE) sources,

including biomass, biogas, solar power, wind

power and geothermal power, enabling ac-

cess to cleaner energy on the grid.



Smart meters can provide near-instant

data on supply and demand levels. By

combining this information with real-time

markets, an analytics engine can make the

grid intelligent enough to manage loads

and provide convenient reconciliation of

anything that is produced and consumed

anywhere.

This will enable the distributed energy to

be precisely billed, benefiting customers

for every unit of energy produced in their

premises. Utilities can better manage

peak loads by having access to multiple

energy sources with reduced carbon foot

print for the grid. However, due to a higher

variability in generation versus demand,

dynamic price negotiations within the grid

would be required, necessitating demand

response capabilities. Energy storage will

become quite critical due to the variability

of renewable sources.

Predictive analytics can be applied to the

problem of energy storage to forecast

demand spikes and optimize energy

storage and distribution systems for

renewable sources. Combined resources

can then be used for managing demand

response or determining how surplus

energy can be traded in a broader

electricity market. Analytics can also

forecast how energy storage systems are

used on a daily basis so the systems can

be properly sized for a building’s energy

demand, thus avoiding any underutilized

storage capacity.

Analytics will act as an enabler for better

response mechanisms through integrating

and reconciliation of various data sources:

• Accurate forecasting: Widespread

instrumentation and advanced computer

models allow system operators to better

predict and manage renewable energy

variability and uncertainty.

• Smart inverters with auto-switch:

Inverters and other power electronics can

provide control to system operators to

automatically provide some level of grid

support. Auto-switching between sources

can be modelled on historic consumption

trends and weather forecast, such as

switching to solar on sunny days or wind

turbines on windy days to help meet the

peak demand from alternate sources.



• Integrated storage: Smart storage can

help reduce short-term variations in

renewable output, and also manage

mismatches in supply and demand.

• Real-time system management:

Instrumentation and control equipment

across the transmission and distributions

networks will allow system operators

to have real-time awareness of system

conditions. This will also enable the ability

to actively manage grid behaviour, as well

as identify and resolve losses and theft.

• Distribution network planning: Combining

data from grid meters, smart meters and

inline sensors along with geographical

data can provide a real-time network plot

illustrating key line parameters including

voltage, real and reactive power,

percentage loading and other variables.

This enables better monitoring, reliable

system operation and better customer

service in the form of faster outage

restoration and automated alerts. This

will eventually lead to fewer customer

contacts and complaints.

• Differential pricing basis generation mode:

It is imperative that renewable energy

sources have different pricing per unit.

This depends on the number of sources

and their mode of operation, which

is why it is critical to have differential

pricing. Analytical systems will enable

establishing a weight-based model

for this differential pricing after due

consideration of all factors. Such pricing

models will incentivize more consumers

to actively participate in distributed

generation, and eventually make it easier

for utilities to reduce dependency on

traditional sources

Gapa Island in South Korea is an example

of self-sufficient renewable energy

deployment in smart grid. The island, with

a size of 8.5km² and a resident population

of 281 in 2012, had wind and solar

generation systems of 500 kW and 111 kW,

respectively, complimented by a 1-MW

lithium-ion battery. This in turn replaced a

450-KW diesel generator. This combination

has made Gapa a carbon-free electricity

system. The project benefits are estimated

at $415,000 avoided fuel costs and an

annual reduction of more than 750 tons of

CO2 emissions.



Information flow across the industry value chain Smart metering and connected homes will

empower customers, but only if they are

kept engaged. Also, regulatory pressure

is mounting for utilities to enhance

customer experiences by keeping things

simple without compromising on adopting

technology. High levels of stakeholder

engagements will require delivering

seamless experiences across utility

channels by providing choice, consistency,

context and continuity for everyone.

Analytics can enhance transparency,

communication and accessibility among

various stakeholders in the utility chain:

• Faster communication of breakdown

and restoration: Utilities can determine

the location of breakdowns using GPS

coordinates, along with which crew

and equipment is well-suited for the

necessary repairs. This information could

be shared with its service team. This type

of information and analysis enables utility

to accurately provide an estimated time

of restoration to the customers impacted,

often even before they realize there is a

problem.

• Complaints/query turnaround time:

Customers can receive auto alerts of

a breakdown to update them of the

expected resolution time, effort and

grievance compensation for similar

breakdowns.

• Tariffs propositions transparency: Details

corresponding to cost for customers of

competitors with similar consumption

levels will be available and accessible

across the board for tariff quotes.

• Improved safety: Real-time analysis of

customer usage enables detection on

unusual spikes that indicate safety risk,

thus allowing quick identification and

action. Effective monitoring and proactive

maintenance of utilities assets is made

possible with predictive analytics models

incorporating the make, maintenance

schedules and energy usage of assets in

the grid.

• Vulnerable customers: Superior customer

segmentation can be facilitated by smart

data to enable accurate identification of

vulnerable customers and proactively

address their concerns, suggesting

suitable devices for their individual



needs. This would also help providers in

proactively addressing the targets set by

regulators regarding social obligations.

• Governance reports: Smart metering

data enables the automated preparation

of detailed governance reports. By

employing suitable data architecture for

the smart meter data, aggregate figures

can be pulled automatically, eliminating

manual effort for creating such detailed

reports.

• Fines and penalty reduction: Timely and

proactively addressing issues, enabled

by data-driven insights from smart meter,

will bring down the number of escalated

issues and regulatory interventions. This

reduces the cost of fines and penalties.

ConclusionTo conclude, the advent of smart meters

provides significant opportunities for

utilities stakeholders and other industries

through effective, collaborative partnership.

However, in order to realize these benefits,

there are a few challenges which utilities

players need to successfully address.

The potential benefits for successfully

implementing smart meter programs

significantly outweigh these challenges. Be

it enhanced customer experience, superior

financial performance or better industry

governance, assessing the scale of these

opportunities helps utilize smart meters

more effectively.

Technologicalchallenge

Governmentregulations

Customerreceptiveness

Data privacy &security risks

Including wifi connectivity is becoming imperative for all homes. Better data management techniques are required to store and manage a high volume of data and to manage the different flows.

Better consumer protection and eco-friendly regulations must be established to harness the full potential of smart metering. Authorities and regulators must draft policies that foster long-term development, aim to remove barriers for renewables and ensure controls on access and use of smart meter data.

Addressing key customer concerns on the interoperability of smart meters across providers, data usage and other concerns would help in increasing the level of openness among consumers towards smart metering.

While greater control is given to consumers through smart metering and advanced features provided by AMI, the associated security risks must be addressed. Aside from data protection, there is also a broader security concern of hackers infiltrating the networks of connected homes.

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monetizing opportunities for smart meter data - …...[ monetizing opportunities for smart meter...

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