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Data-driven a nalytics - PowerPoint Presentation

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Data-driven a nalytics - PPT Presentation

for u nderstanding u tility customer behavior Arjen Zondervan Alliander Liander Klant amp Markt Maarten Wolf Alliander Liandon Sasha Aravkin IBM Research 1 Customer Intelligence ID: 792438

customer predict grid data predict customer data grid alliander customers analytics energy model distribution potential seri growth behavior flevoland

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Presentation Transcript

Slide1

Data-driven analytics for understanding utility customer behavior

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

1

Customer Intelligence

Slide2

Alliander

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

2

Alliander

Intro

Slide3

Alliander

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

SERI

C

ollaboration

S

marter

E

nergy

R

esearch Institute (SERI)Collaboration of three utility companies Alliander, DTE (Detroit) and Hydro QuebecIBM 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 ITAlliander 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.

Slide4

Necessity: Customer sideCustomers role is changing

From passive loads to ‘prosumers’Generating energy and supplying back to the gridOrganizing 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.

4

Why Customer Intelligence?

Possibility: Data analytics side

More data

M

ore 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.

Slide5

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

Example

A

pplications 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

Slide6

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

6

PV-model

6

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.

Slide7

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 distribution

installed PV

household demographics

logistic regression

estimate of PV growth

installed PV

household demographics

subsidy

PV price development

economic prospects

MC sampling

expected

PV

distribution

growth curves PV installation

s

urvival analysis

input

model

output

PV probability per household

7

PV-model

Datum

Titel van de presentatie

Slide8

household with specific characteristics

…PV probability

Flevoland

PV

penetration

8

Use 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

Slide9

Flevoland PV penetration9

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

risk map

Slide10

Questions?