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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Soohyun Kim & Youngghyu Sun & Seongwoo Lee & Joonho Seon & Byungsun Hwang & Jeongho Kim & Jinwook Kim & Kyounghun Kim & Jinyoung Kim, 2024. "Data-Driven Approaches for Energy Theft Detection: A Comprehensive Review," Energies, MDPI, vol. 17(12), pages 1-23, June.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:12:p:3057-:d:1419203
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    References listed on IDEAS

    as
    1. Mahmoud M. Badr & Mohamed I. Ibrahem & Hisham A. Kholidy & Mostafa M. Fouda & Muhammad Ismail, 2023. "Review of the Data-Driven Methods for Electricity Fraud Detection in Smart Metering Systems," Energies, MDPI, vol. 16(6), pages 1-18, March.
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