Non-Intrusive Load Monitoring (NILM) using Deep Neural Networks: A Review

Conference paper

Azad, M. I., Rajabi, R. and Estebsari, A. (2023). Non-Intrusive Load Monitoring (NILM) using Deep Neural Networks: A Review. IEEE 23rd International Conference on Environment and Electrical Engineering (EEEIC). Madrid 06 - 09 Jun 2023 Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/EEEIC/ICPSEurope57605.2023.10194770
AuthorsAzad, M. I., Rajabi, R. and Estebsari, A.
TypeConference paper
Abstract

Demand-side management now encompasses more residential loads. To efficiently apply demand response strategies, it's essential to periodically observe the contribution of various domestic appliances to total energy consumption. Non-intrusive load monitoring (NILM), also known as load disaggregation, is a method for decomposing the total energy consumption profile into individual appliance load profiles within the household. It has multiple applications in demand-side management, energy consumption monitoring, and analysis. Various methods, including machine learning and deep learning, have been used to implement and improve NILM algorithms. This paper reviews some recent NILM methods based on deep learning and introduces the most accurate methods for residential loads. It summarizes public databases for NILM evaluation and compares methods using standard performance metrics.

KeywordsSmart Grids, NILM, Deep Learning, Energy Management.
Year2023
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Digital Object Identifier (DOI)https://doi.org/10.1109/EEEIC/ICPSEurope57605.2023.10194770
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Print03 Aug 2023
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AcceptedMay 2023
Deposited17 Aug 2023
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© © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

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