Enhancing energy efficiency in the residential sector with smart meter data analytics
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Tailored energy efficiency campaigns that make use of household-specific information can trigger substantial energy savings in the residential sector. The information required for such campaigns, however, is often missing. We show that utility companies can extract that information from smart meter data using machine learning. We derive 133 features from smart meter and weather data and use the Random Forest classifier that allows us to recognize 19 household classes related to 11 household characteristics (e.g., electric heating, size of dwelling) with an accuracy of up to 95% (69% on average). The results indicate that even datasets with an hourly or daily resolution are sufficient to impute key household characteristics with decent accuracy and that data from different yearly seasons does not considerably influence the classification performance. Furthermore, we demonstrate that a small training data set consisting of only 200 households already reaches a good performance. Our work may serve as benchmark for upcoming, similar research on smart meter data and provide guidance for practitioners for estimating the efforts of implementing such analytics solutions.
KeywordsGreen information systems Decision support systems Data analytics Energy efficiency Sustainability Classification
JEL classificationC80 D10 M310 Q20 R20
We thank Ilya Kozlovskiy for his contribution to the data analysis in this study. We kindly acknowledge financial support from the Swiss Federal Office of Energy (Grant numbers SI/501053-01, SI/501202-01) and want to thank Michael Moser and Roland Brüniger for the very helpful comments during the research project.
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