A Scoping Review of Energy Load Disaggregation

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Abstract

Energy load disaggregation can contribute to balancing power grids by enhancing the effectiveness of demand-side management and promoting electricity-saving behavior through increased consumer awareness. However, the field currently lacks a comprehensive overview. To address this gap, this paper conducts a scoping review of load disaggregation domains, data types, and methods, by assessing 72 full-text journal articles. The findings reveal that domestic electricity consumption is the most researched area, while others, such as industrial load disaggregation, are rarely discussed. The majority of research uses relatively low-frequency data, sampled between 1 and 60 s. A wide variety of methods are used, and artificial neural networks are the most common, followed by optimization strategies, Hidden Markov Models, and Graph Signal Processing approaches.

OriginalsprogEngelsk
TitelProgress in Artificial Intelligence - 22nd EPIA Conference on Artificial Intelligence, EPIA 2023, Proceedings : 22nd EPIA Conference on Artificial Intelligence, EPIA 2023, Faial Island, Azores, September 5–8, 2023, Proceedings, Part II
RedaktørerNuno Moniz, Zita Vale, José Cascalho, Catarina Silva, Raquel Sebastião
Vol/bind2
ForlagSpringer
Publikationsdato2023
Sider209–221
ISBN (Trykt)978-3-031-49010-1
ISBN (Elektronisk)978-3-031-49011-8
DOI
StatusUdgivet - 2023
BegivenhedThe 22nd Portuguese conference on artificial intelligence -
Varighed: 5. sep. 20238. sep. 2023

Konference

KonferenceThe 22nd Portuguese conference on artificial intelligence
Periode05/09/202308/09/2023
NavnLecture Notes in Computer Science
Vol/bind14116
ISSN0302-9743

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