TY - GEN
T1 - A Scoping Review of Energy Load Disaggregation
AU - Tolnai, Balázs András
AU - Jørgensen, Bo Nørregaard
AU - Ma, Zheng Grace
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Energy load disaggregation
KW - scoping review
KW - load disaggregation methods
KW - data and data source
KW - Scoping review
KW - Data and data source
KW - Load disaggregation methods
U2 - 10.1007/978-3-031-49011-8_17
DO - 10.1007/978-3-031-49011-8_17
M3 - Article in proceedings
SN - 978-3-031-49010-1
VL - 2
T3 - Lecture Notes in Computer Science
SP - 209
EP - 221
BT - Progress in Artificial Intelligence - 22nd EPIA Conference on Artificial Intelligence, EPIA 2023, Proceedings
A2 - Moniz, Nuno
A2 - Vale, Zita
A2 - Cascalho, José
A2 - Silva, Catarina
A2 - Sebastião, Raquel
PB - Springer
T2 - The 22nd Portuguese conference on artificial intelligence
Y2 - 5 September 2023 through 8 September 2023
ER -