Statistical and Electrical Features Evaluation for Electrical Appliances Energy Disaggregation

Pascal Schirmer, Iosif Mporas

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)
37 Downloads (Pure)

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.
Original languageEnglish
Article number3222
Number of pages14
JournalSustainability
Volume11
Issue number11
DOIs
Publication statusPublished - 11 Jun 2019

Keywords

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

Fingerprint

Dive into the research topics of 'Statistical and Electrical Features Evaluation for Electrical Appliances Energy Disaggregation'. Together they form a unique fingerprint.

Cite this