1. Revealing Household Characteristics from Smart Meter Data
Beckel, Christian; Mariya Sodenkmap; Ilya Kozlovsky, Sadamori, Lenya; Santini, Silvia; Staake, Thorsten
Dagstuhl | February 2015

2. At the Bits to Energy Lab, we develop and probe data-driven energy efficiency services.
B2E is an initiative of ETH Zurich and the Universities of Bamberg and St. Gallen
We team up with companies and government agencies to
develop data-driven energy services for electricity, heat, and mobility applications
probe and evaluate the services in field tests
advance theory in machine learning and behavioral economics
Open positions

3. Consumption data (and smart grid data in particular) contains plenty of valuable information.
Measure /
Retrieve Data
Recognize Patterns
Place Interventions
Measure & Adjust W

4. Let as look into some real world questions energy retailers ask.
We have 450’000 household customers.
We want to comply with the efficiency regulations (say, make customers save 1.5% p.a.) at minimal cost. Whom to address?
We want to sell “green electricity” to families with kids!
Lets focus on young persons to make them accept their bill online!
We want to promote PV-systems among high-income home owners!
Lets market home automation products to young professionals!
Maybe in the future: Who among our customers has both, load shifting potential and characteristics XYZ that make her likely to participate in a load shifting campaigns?
 But how to address the right households without knowing what is behind each contract number?

5. We now take the perspective of a utility company and try to identify their customers’ household characteristics…
Bildquelle: iStockohoto/Thinkstock

6. ... by using data that contains the information and that is (or will be) available.
Load profiles 2 kW 1 6 12 18 24
time
Quelle: Beckel, Bits to Energy Lab

7. Example project SM-DA: Transforming load profiles in customer insight.
Predated
properties
Load profiles
Classification
Single
Floor area
...
Feature Extraction
Machine Learning Algorithm
Feature extraction
22 features (e.g., consumption averages, ratios)
Machine learning algorithms
4 classifiers (kNN, LDA, Mahalanobis, SVM)
Prediction
12 household properties
Discrete sets of classes
Ground Truth
Quelle: C. Beckel, Bits to Energy Lab

8. This is one of the first results, using only 30-min data of one week.
Accuracy
Source: I. Kozlovskiy, Bits to Energy Lab

9. The methods are not limited to smart meter data and are widely applied in the field.
Data Services
Relevant and actionable customer insights and intelligence
Increasing upselling and retention
Reducing cost to serve
CRM
Billing
Data
Analytics
Customer Engagement Platform
Customer interaction
Customer experience
Reaching 5-10% of residential customers
Open source
(Smart) Metering
Training Data
Energy Efficiency Mailing
Energy conservation at lowest- in-class cost per kWh
Reaching % of customers

10. Some findings from tests with more than 100‘000 households
Cost per kWh saved, portal registration, etc., down by 50 to 60% (!)
Performance: Despite big data not a big issue
Accuracy: Sufficient given an acceptable trade-off between precision and recall
Privacy: Virtually no concerns by the customers (“clear customer value visible”)
Many application: automatic energy consulting, augmented bills, use of data for home automation systems
Research: (still) better accuracy, use of other data sources, suitability of test data for other regions, etc.

11. Thank you very much for your attention.
Energy Efficient Systems Group University of Bamberg