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Monetizingopportunities for
smart meter data
An EXL whitepaper
Written by
Vikas KumarAssistant Vice PresidentDecision Analyticslookdeeper@exlservice.com
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
2 © 2016 ExlService Holdings, Inc.
[ Monetizing opportunities for smart meter data ]
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
3 © 2016 ExlService Holdings, Inc.
[ Monetizing opportunities for smart meter data ]
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
4 © 2016 ExlService Holdings, Inc.
[ Monetizing opportunities for smart meter data ]
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
5 © 2016 ExlService Holdings, Inc.
[ Monetizing opportunities for smart meter data ]
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
6 © 2016 ExlService Holdings, Inc.
[ Monetizing opportunities for smart meter data ]
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.
7 © 2016 ExlService Holdings, Inc.
[ Monetizing opportunities for smart meter data ]
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.
8 © 2016 ExlService Holdings, Inc.
[ Monetizing opportunities for smart meter data ]
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.
9 © 2016 ExlService Holdings, Inc.
[ Monetizing opportunities for smart meter data ]
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
10 © 2016 ExlService Holdings, Inc.
[ Monetizing opportunities for smart meter data ]
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
11 © 2016 ExlService Holdings, Inc.
[ Monetizing opportunities for smart meter data ]
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.
12 © 2016 ExlService Holdings, Inc.
[ Monetizing opportunities for smart meter data ]
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.
13 © 2016 ExlService Holdings, Inc.
[ Monetizing opportunities for smart meter data ]
• 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.
14 © 2016 ExlService Holdings, Inc.
[ Monetizing opportunities for smart meter data ]
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
15 © 2016 ExlService Holdings, Inc.
[ Monetizing opportunities for smart meter data ]
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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