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Data-Driven Approaches for Energy Theft Detection: A Comprehensive Review.

Authors :
Kim, Soohyun
Sun, Youngghyu
Lee, Seongwoo
Seon, Joonho
Hwang, Byungsun
Kim, Jeongho
Kim, Jinwook
Kim, Kyounghun
Kim, Jinyoung
Source :
Energies (19961073). Jun2024, Vol. 17 Issue 12, p3057. 22p.
Publication Year :
2024

Abstract

The transition to smart grids has served to transform traditional power systems into data-driven power systems. The purpose of this transition is to enable effective energy management and system reliability through an analysis that is centered on energy information. However, energy theft caused by vulnerabilities in the data collected from smart meters is emerging as a primary threat to the stability and profitability of power systems. Therefore, various methodologies have been proposed for energy theft detection (ETD), but many of them are challenging to use effectively due to the limitations of energy theft datasets. This paper provides a comprehensive review of ETD methods, highlighting the limitations of current datasets and technical approaches to improve training datasets and the ETD in smart grids. Furthermore, future research directions and open issues from the perspective of generative AI-based ETD are discussed, and the potential of generative AI in addressing dataset limitations and enhancing ETD robustness is emphasized. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
19961073
Volume :
17
Issue :
12
Database :
Academic Search Index
Journal :
Energies (19961073)
Publication Type :
Academic Journal
Accession number :
178154943
Full Text :
https://doi.org/10.3390/en17123057