Non-Intrusive Appliance Identification Using Machine Learning and Time-Domain Features

Hajer Alyammahi, Panos Liatsis

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

This paper addresses the challenge of nonintrusive load monitoring (NILM), i.e., identifying the combination of turned-on electrical appliances using a single measurement, which is the aggregated power signal. An automated appliance status labelling method based on new event detection is proposed. Time domain statistical features are calculated using various window lengths. The optimum window size is selected and three well-known and widely used machine learning algorithms, i.e., K-Nearest-Neighbors (KNN), Bagged trees, and Boosted trees are used for classification in the context of power consumption disaggregation. The Reference Energy Disaggregation Dataset (REDD) is used in the experiments to evaluate the performance of the classifiers. The simulation results demonstrate the importance of selecting the appropriate window length and optimizing the classifier configuration. Compared to the state-of-the-art, a very competitive performance, coupled with low computational complexity, is achieved with F1-score values over 97%, when considering all the appliances in the REDD dataset.

Original languageBritish English
Title of host publication2022 29th International Conference on Systems, Signals and Image Processing, IWSSIP 2022
EditorsGalia Marinova
PublisherIEEE Computer Society
ISBN (Electronic)9781665495783
DOIs
StatePublished - 2022
Event29th International Conference on Systems, Signals and Image Processing, IWSSIP 2022 - Sofia, Bulgaria
Duration: 1 Jun 20223 Jun 2022

Publication series

NameInternational Conference on Systems, Signals, and Image Processing
Volume2022-June
ISSN (Print)2157-8672
ISSN (Electronic)2157-8702

Conference

Conference29th International Conference on Systems, Signals and Image Processing, IWSSIP 2022
Country/TerritoryBulgaria
CitySofia
Period1/06/223/06/22

Keywords

  • Appliance identification
  • classification
  • machine learning
  • nonintrusive load monitoring
  • smart homes

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