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 2 Department of Computer Science, Chair for Application & Middleware Systems

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 3 Department of Computer Science, Chair for Application & Middleware Systems

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 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 4 Department of Computer Science, Chair for Application & Middleware Systems

Technische Universität München No joke: http://www.unijobs.at/nebenjobs/vorschau/248788 5 Department of Computer Science, Chair for Application & Middleware Systems

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 6 Department of Computer Science, Chair for Application & Middleware Systems

Technische Universität München Windowing Prediction Base Actuals Interval Forecast Backlog Minutes Minutes 7 Department of Computer Science, Chair for Application & Middleware Systems

Technische Universität München Machine learning pipeline Extraction • Raw CSV files are extracted line by line • Learn horizon • Prediction horizon Windowing • Every increment, e.g., 30 minutes Map • Results per window Prediction Map • MAPE Error • RMSE calculation • ... Fold or Map Reduce 8 Department of Computer Science, Chair for Application & Middleware Systems

Technische Universität München Feature extraction • Statistical • Discrete – Min, Max, Mean, Variance, – Last state on/off Standard deviation – Switched on/off • Descriptive statistics • Timestamp extraction – Skewness, Kurtosis – Week day • Financial – Hour of the day – Momentum, WilliamsR 9 Department of Computer Science, Chair for Application & Middleware Systems

Technische Universität München 10 Department of Computer Science, Chair for Application & Middleware Systems

Technische Universität München Results 11 Department of Computer Science, Chair for Application & Middleware Systems

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 12 Department of Computer Science, Chair for Application & Middleware Systems

Technische Universität München Future work • Many problems, … • But it’s Opensource  • Already done • Own software for getting the results • Own backend • Current stage Energy Monitoring Node • Gain experience Department of Computer Science, Chair for Application & Middleware Systems

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. 5 th 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 14 Department of Computer Science, Chair for Application & Middleware Systems