Statistical and Electrical Features Evaluation for Electrical Appliances Energy Disaggregation

Pascal A. Schirmer and Iosif Mporas
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Pascal A. Schirmer: School of Engineering and Computer Science, University of Hertfordshire, Hatfield AL10 9AB, UK
Iosif Mporas: School of Engineering and Computer Science, University of Hertfordshire, Hatfield AL10 9AB, UK

Sustainability, 2019, vol. 11, issue 11, 1-14

Abstract: 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.

Keywords: non-intrusive load monitoring (NILM); energy disaggregation; feature selection (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:11:y:2019:i:11:p:3222-:d:238734

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