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

Soohyun Kim, Youngghyu Sun, Seongwoo Lee, Joonho Seon, Byungsun Hwang, Jeongho Kim, Jinwook Kim, Kyounghun Kim and Jinyoung Kim ()
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Soohyun Kim: Department of Electronic Convergence Engineering, Kwangwoon University, Seoul 01897, Republic of Korea
Youngghyu Sun: Research and Development Department, SMART EVER, Co., Ltd., Seoul 01886, Republic of Korea
Seongwoo Lee: Department of Electronic Convergence Engineering, Kwangwoon University, Seoul 01897, Republic of Korea
Joonho Seon: Department of Electronic Convergence Engineering, Kwangwoon University, Seoul 01897, Republic of Korea
Byungsun Hwang: Department of Electronic Convergence Engineering, Kwangwoon University, Seoul 01897, Republic of Korea
Jeongho Kim: Department of Electronic Convergence Engineering, Kwangwoon University, Seoul 01897, Republic of Korea
Jinwook Kim: Department of Electronic Convergence Engineering, Kwangwoon University, Seoul 01897, Republic of Korea
Kyounghun Kim: Department of Electronic Convergence Engineering, Kwangwoon University, Seoul 01897, Republic of Korea
Jinyoung Kim: Department of Electronic Convergence Engineering, Kwangwoon University, Seoul 01897, Republic of Korea

Energies, 2024, vol. 17, issue 12, 1-23

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.

Keywords: energy theft detection; data-driven approach; generative AI; supervised learning; semi-supervised learning; smart meter (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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