Transcript Presentation 1

Customer Intelligence
Data-driven analytics for understanding utility customer behavior
Arjen Zondervan (Alliander, Liander Klant & Markt)
Maarten Wolf (Alliander, Liandon)
Sasha Aravkin (IBM Research)
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Alliander Intro
Alliander is an energy network group, distributing electricity and gas to
around three million households in the Netherlands.
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SERI Collaboration
Alliander joined the SERI collaboration in 2012 to develop an advanced
analytics competence and create more value from analytics.
•
Smarter Energy Research Institute (SERI)
•
Collaboration of three utility companies Alliander, DTE (Detroit) and
Hydro Quebec
•
IBM Research (Watson Lab) as knowledge partner on advanced
analytics and facilitator of the collaboration
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Multidisciplinary team within Alliander: 3 Business units
‘Customer & Market’ (‘Klant & Markt’), Asset Management and IT
•
Alliander SERI team works in two streams: Asset Management
models and Customer Intelligence (CI, topic of this presentation)
•
Goal of the project for Alliander: develop an advanced analytics
competence, while creating business value through the models
that are developed.
Why Customer Intelligence?
It is both necessary and possible for grid operators to predict the
behavior of their customers.
Necessity: Customer side
Possibility: Data analytics side
Customers role is changing
• From passive loads to ‘prosumers’
• Generating energy and supplying back to the grid
• Organizing themselves in cooperatives
• Adopting possibly disruptive technologies
(PV/EV/heat pumps)
• More vocal: e.g. social media
More data
• More and more data about our customers and our
assets (digitalization)
• More and more external data can be acquired at
decreasing cost
• Better IT systems to store, link and prepare data
for analysis
We want to influence customer behavior more
• Energy savings programs
• Support sustainable energy
• Demand/response and peakshaving
• Smart meter roll out
Better analytics
• Better analytics methods (data mining algorithms)
• From looking back and describing to predicting and
optimizing based on that prediction
• Better tools and IT to handle large data sets
It is both necessary and possible for grid operators to predict the behavior of their customers
and adapt their strategy and their operations to these predictions.
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Example Applications of CI
Predicting customer behavior has numerous applications which can
deliver serious business value for grid operators.
Focus of Alliander SERI CI team the last 2 years
Energy Saving Potential
 Predict energy savings potential of
(groups of) customers
 Use for savings project location
selection or providing individual
benchmarks
Fraud detection
 Predict based on usage data and
customer data which customers/
areas are high risk for hosting
illegal weed growing operations
Adoption of PV/EV/heatpumps
 Predict which customers are most
likely to adopt EV/PV/heatpumps
 Model spread of new technologies
over service area to prepare the
grid
Future focus of Alliander SERI CI team
Customer Satisfaction
 Predict which customers/group
have potential/risk for increasing/
decreasing customer satisfaction
 Determine which variables are most
predictive of customer satisfaction
Demand Response
 Predict which customers will
respond to demand/response
programs
 Predict the shift in demand
achieved through e.g. variable rates
Customer Contact
 Predict which (group of)
customers is likely to contact us,
when and why
 Pre-empt or optimize the contact
for costs or customer satisfaction
PV-model
Predicting PV-adoption allows Alliander to support the energy transition
and prepare its assets for the additional load.
• Popularity of solar panels (PV) has increased dramatically over the last couple of years.
Situation
Complication
Solution
Use Case
• This growth is predicted to continue by a factor 4 to 16 in 2020.
• In order to stimulate the energy transition as effectively as possible, we need to know
where the highest potential for PV is.
• The adoption of PV causes a very local significant extra load on the grid with possible
disruptions and outages as a result.
• Building a predictive model based on customer data to predict which customers are most
likely to adopt PV and predict the spread of PV over the Liander grid over time.
• Use the model to predict the PV distribution in the province of Flevoland up to 2030 to assess
the impact on the grid and identify potential problems.
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PV-model
To predict the location of future PV installation we have developed a
distribution model. Model development is aimed at growth prediction.
PV probability
input
model
output
installed PV
logistic
regression
PV probability
per household
PV distribution
household
demographics
MC sampling
expected PV
distribution
estimate of PV
growth
PV predicted growth – how much and where
installed PV
subsidy
economic
prospects
survival
analysis
household
demographics
growth curves
PV installation
PV price
development
Datum
Titel van de presentatie
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Flevoland PV penetration
Use case: Predict the PV distribution in Flevoland to assess the impact on
the grid and identify potential problems for the grid.
household
with specific
characteristics
…
PV probability
…
PV
distribution
PV scenarios
household
distribution
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Flevoland PV penetration
Use case: Predict the PV distribution in Flevoland to assess the impact on
the grid and identify potential problems for the grid.
risk map
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Questions?