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
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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
Slide2Alliander
is an energy network group, distributing electricity and gas to around three million households in the Netherlands.
2
Alliander
Intro
Slide3Alliander
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.
Slide4Necessity: 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.
Slide5Predicting 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
Slide6Predicting 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.
Slide7To 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
Slide8household 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
Slide9Flevoland 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
Slide10Questions?