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Recent Trends and Issues of Energy Management Systems Using Machine Learning

Author

Listed:
  • 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)

  • Soohyun Kim

    (Department of Electronic Convergence Engineering, Kwangwoon University, Seoul 01897, Republic of Korea)

  • Youngghyu Sun

    (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

Energy management systems (EMSs) are regarded as essential components within smart grids. In pursuit of efficiency, reliability, stability, and sustainability, an integrated EMS empowered by machine learning (ML) has been addressed as a promising solution. A comprehensive review of current literature and trends has been conducted with a focus on key areas, such as distributed energy resources, energy management information systems, energy storage systems, energy trading risk management systems, demand-side management systems, grid automation, and self-healing systems. The application of ML in EMS is discussed, highlighting enhancements in data analytics, improvements in system stability, facilitation of efficient energy distribution and optimization of energy flow. Moreover, architectural frameworks, operational constraints, and challenging issues in ML-based EMS are explored by focusing on its effectiveness, efficiency, and suitability. This paper is intended to provide valuable insights into the future of EMS.

Suggested Citation

  • Seongwoo Lee & Joonho Seon & Byungsun Hwang & Soohyun Kim & Youngghyu Sun & Jinyoung Kim, 2024. "Recent Trends and Issues of Energy Management Systems Using Machine Learning," Energies, MDPI, vol. 17(3), pages 1-24, January.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:3:p:624-:d:1327970
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    References listed on IDEAS

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