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

被引:0
|
作者
Kim, Soohyun [1 ]
Sun, Youngghyu [2 ]
Lee, Seongwoo [1 ]
Seon, Joonho [1 ]
Hwang, Byungsun [1 ]
Kim, Jeongho [1 ]
Kim, Jinwook [1 ]
Kim, Kyounghun [1 ]
Kim, Jinyoung [1 ]
机构
[1] Kwangwoon Univ, Dept Elect Convergence Engn, Seoul 01897, South Korea
[2] SMART EVER Co Ltd, Res & Dev Dept, Seoul 01886, South Korea
关键词
energy theft detection; data-driven approach; generative AI; supervised learning; semi-supervised learning; smart meter; ELECTRICITY THEFT; CYBER-ATTACKS;
D O I
10.3390/en17123057
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
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.
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页数:22
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