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Statistical and Electrical Features Evaluation for Electrical Appliances Energy Disaggregation

by *,†,‡ and †,‡
School of Engineering and Computer Science, University of Hertfordshire, Hatfield AL10 9AB, UK
Author to whom correspondence should be addressed.
Current address: University of Hertfordshire, College Lane, Hatfield AL10 9AB, UK.
These authors contributed equally to this work.
Sustainability 2019, 11(11), 3222;
Received: 10 May 2019 / Revised: 30 May 2019 / Accepted: 5 June 2019 / Published: 11 June 2019
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. View Full-Text
Keywords: non-intrusive load monitoring (NILM); energy disaggregation; feature selection non-intrusive load monitoring (NILM); energy disaggregation; feature selection
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Schirmer, P.A.; Mporas, I. Statistical and Electrical Features Evaluation for Electrical Appliances Energy Disaggregation. Sustainability 2019, 11, 3222.

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