Towards Applicability: A Comparative Study onNon-Intrusive Load Monitoring Algorithms

Huamin Ren, Filippo Maria Bianchi, Jingyue Li, Rasmus Løvenstein Olsen, Robert Jense, Stian Normann Anfinsen

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

Abstract

Non-intrusive load monitoring is an important en-ergy disaggregation technology, which can provide appliance-levelconsumption estimation given a series of total consumption overtime, aiding users green awareness and living as well as devicefault detection. Despite of the claimed performance, an insightfulanalysis on practical perspectives is still lacking. In this paperwe therefore propose a unified pipeline to conduct comparativeexperiments on four representative state-of-the-art algorithms.We further provide an in-depth look at two crucial factorsaffecting algorithms’ feasibility in practice, namely sampling rateand transferability, as well as the algorithms’ performance onenergy disaggregation.
Original languageEnglish
Title of host publication2021 IEEE International Conference on Consumer Electronics (ICCE)
Number of pages5
PublisherIEEE
Publication date10 Jan 2021
Pages1-5
Article number9427652
ISBN (Print)978-1-7281-9767-8
ISBN (Electronic)978-1-7281-9766-1
DOIs
Publication statusPublished - 10 Jan 2021
Event 2021 IEEE International Conference on Consumer Electronics (ICCE) - Las Vegas, United States
Duration: 10 Jan 202112 Jan 2021

Conference

Conference 2021 IEEE International Conference on Consumer Electronics (ICCE)
CountryUnited States
CityLas Vegas
Period10/01/202112/01/2021
SeriesIEEE International Conference on Consumer Electronics, (ICCE)
ISSN2158-3994

Keywords

  • NILM
  • deep learning
  • energy disaggregation

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