Statistical and Electrical Features Evaluation for Electrical Appliances Energy Disaggregation

Pascal Schirmer, Iosif Mporas

    Research output: Contribution to journalArticlepeer-review

    22 Citations (Scopus)
    29 Downloads (Pure)


    In this paper we evaluate several well-known and widely used machine learning algorithms for regression in the energy disaggregation task. Specifically, the Non-Intrusive Load Monitoring approach was considered and the K-Nearest-Neighbours, Support Vector Machines, Deep Neural Networks and Random Forest algorithms were evaluated across five datasets using seven different sets of statistical and electrical features. The experimental results demonstrated the importance of selecting both appropriate features and regression algorithms. Analysis on device level showed that linear devices can be disaggregated using statistical features, while for non-linear devices the use of electrical features significantly improves the disaggregation accuracy, as non-linear appliances have non-sinusoidal current draw and thus cannot be well parametrized only by their active power consumption. The best performance in terms of energy disaggregation accuracy was achieved by the Random Forest regression algorithm.
    Original languageEnglish
    Article number3222
    Number of pages14
    Issue number11
    Publication statusPublished - 11 Jun 2019


    • Energy disaggregation
    • Feature selection
    • Non-intrusive load monitoring (NILM)


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