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Data-driven analytics for understanding utility customer behavior

Arjen Zondervan (Alliander, Liander Klant & Markt)Maarten Wolf (Alliander, Liandon)Sasha Aravkin (IBM Research)

Customer Intelligence

Alliander is an energy network group, distributing electricity and gas to around three million households in the Netherlands.

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Alliander Intro

Alliander joined the SERI collaboration in 2012 to develop an advanced analytics competence and create more value from analytics.

SERI Collaboration

• 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• 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.

Necessity: Customer 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

We want to influence customer behavior more• Energy savings programs• Support sustainable energy • Demand/response and peakshaving• Smart meter roll out

It is both necessary and possible for grid operators to predict the behavior of their customers.

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Why Customer Intelligence?

Possibility: Data analytics side

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

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.

Predicting customer behavior has numerous applications which can deliver serious business value for grid operators.

Example Applications of CI

Predict based on usage data and customer data which customers/ areas are high risk for hosting illegal weed growing operations

Fraud detection

Predict energy savings potential of (groups of) customers

Use for savings project location selection or providing individual benchmarks

Energy Saving Potential

Predict which customers are most likely to adopt EV/PV/heatpumps

Model spread of new technologies over service area to prepare the grid

Adoption of PV/EV/heatpumps

Focus of Alliander SERI CI team the last 2 years

Predict which customers will respond to demand/response programs

Predict the shift in demand achieved through e.g. variable rates

Demand Response

Predict which customers/group have potential/risk for increasing/ decreasing customer satisfaction

Determine which variables are most predictive of customer satisfaction

Customer Satisfaction

Predict which (group of) customers is likely to contact us, when and why

Pre-empt or optimize the contact for costs or customer satisfaction

Customer Contact

Future focus of Alliander SERI CI team

Predicting PV-adoption allows Alliander to support the energy transition and prepare its assets for the additional load.

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PV-model

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• Popularity of solar panels (PV) has increased dramatically over the last couple of years.

• 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.

Situation

Complication

Solution

Use Case• 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.

To predict the location of future PV installation we have developed a distribution model. Model development is aimed at growth prediction.

PV predicted growth – how much and where

PV probability

PV distributioninstalled PV

household demographics

logistic regression

estimate of PV growth

installed PV

household demographics

subsidy

PV price development

economic prospects

MC samplingexpected PV distribution

growth curves PV installation

survival analysis

input model output

PV probability per household

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PV-model

DatumTitel van de presentatie

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household with specific

characteristics…

PV probability …

Flevoland PV penetrationUse case: Predict the PV distribution in Flevoland to assess the impact on the grid and identify potential problems for the grid.

PV scenarios

household distribution

PV distribution

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Flevoland PV penetrationUse case: Predict the PV distribution in Flevoland to assess the impact on the grid and identify potential problems for the grid.

risk map

Questions?