Household data analytics Dagstuhl, February 2015 Christoph - - PowerPoint PPT Presentation

Technische Universität München

Household data analytics

Dagstuhl, February 2015

Christoph Doblander, Anwar Ul Haq, Christoph Goebel, H.-A. Jacobsen

Technische Universität München Institut für Informatik, Lehrstuhl I13

• Prof. Dr. H.-A. Jacobsen

Alexander von Humboldt-Professor

Technische Universität München

Peer Energy Cloud

Department of Computer Science, Chair for Application & Middleware Systems

2

Technische Universität München

Our contributions within the project

• Energy analytics framework

– Highly scalable framework which uses spare compute resources

• Evaluation of forecasting methods

– Algorithms: SVM, naive bayes, ... – Benchmark: persistence, average load profiles and same as last weekend forecast – Timehorizon: {15, 30, 60, 90 } ahead minutes horizon

• Not shown today

– Anomaly detection – Model based forecasting – Device detection

Department of Computer Science, Chair for Application & Middleware Systems

3

Technische Universität München

Relevance of prediction of a single household

• Increase self sufficiency is a financial benefit
• Hard to justify here in Germany because of high

incentives to feed electricity into the grid

Department of Computer Science, Chair for Application & Middleware Systems

4 Example Austria/Tirol 5kWp cost roughly 9,000 € 1,375 € subsidiary once for investing Feeding into the grid When lucky, OEMAG quota (12 ct/Kwh, maxed out in the first few hours) else contract with a electricity trader between 2.4 – 5 ct/Kwh compared to 12 ct/Kwh in Germany

New Quota 07.01.2015 17:00

Technische Universität München

Department of Computer Science, Chair for Application & Middleware Systems

5

No joke: http://www.unijobs.at/nebenjobs/vorschau/248788

Technische Universität München

Current possibilities

• Miele@Home

– „SG-ready“ – Miele Smart Start

• Extension

– Predict non-controllable loads, schedule the others accordingly – One input of the control is the prediction – => Increase self sufficiency

Department of Computer Science, Chair for Application & Middleware Systems

6

Technische Universität München

Windowing

Department of Computer Science, Chair for Application & Middleware Systems

7 Prediction Base Actuals

Interval Forecast Minutes Backlog Minutes

Technische Universität München

Machine learning pipeline

Extraction

• Raw CSV files are extracted line by line

Windowing

• Learn horizon
• Prediction horizon
• Every increment, e.g., 30 minutes

Prediction

• Results per window

Error calculation

• MAPE
• RMSE
• ...

Department of Computer Science, Chair for Application & Middleware Systems

8

Map Fold or Map Map Reduce

Technische Universität München

Feature extraction

• Statistical

– Min, Max, Mean, Variance, Standard deviation

• Descriptive statistics

– Skewness, Kurtosis

• Financial

– Momentum, WilliamsR

• Discrete

– Last state on/off – Switched on/off

• Timestamp extraction

– Week day – Hour of the day

Department of Computer Science, Chair for Application & Middleware Systems

9

Technische Universität München

Department of Computer Science, Chair for Application & Middleware Systems

10

Technische Universität München

Results

Department of Computer Science, Chair for Application & Middleware Systems

11

Technische Universität München

Results overview and conclusion

• 34 households

– 20 households were chosen for more evaluation – 14 households were rejected because of bad data quality – Benchmark is persistence – Results are preliminary – 70% of the data analysed

• Benchmark can be beaten

– But prediction error is still high

• More features tend to reduce predictionerror in

predictions up to an hour

Department of Computer Science, Chair for Application & Middleware Systems

12

Technische Universität München

Future work

• Many problems, …
• But it’s Opensource 
• Already done
• Own software for getting the results
• Own backend
• Current stage
• Gain experience

Department of Computer Science, Chair for Application & Middleware Systems

Energy Monitoring Node

Technische Universität München

References

• A. Veit, C. Goebel, R. Tidke, C. Doblander, H.-A.
• Jacobsen. Household Electricity Demand Forecasting:

Benchmarking State-of-the-Art Methods. 5th ACM International Conference on Future Energy Systems (ACM e-Energy), Cambridge, UK.

• H. Ziekow, C. Doblander, C. Goebel, H.-A. Jacobsen. Forecasting

Household Electricity Demand with Complex Event Processing: Insights from a Prototypical Solution. 13th ACM International Middleware Conference, Beijing, China. 2013

• H. Ziekow, C. Goebel, J. Strüker, H.-A. Jacobsen. The Potential of

Smart Home Sensors in Forecasting Household Electricity

• Demand. IEEE International Conference on Smart Grid

Communications (SmartGridComm2013), Vancouver, Canada. 2013

Department of Computer Science, Chair for Application & Middleware Systems

14