Recent Trends and Issues of Energy Management Systems Using Machine Learning

Seongwoo Lee, Joonho Seon, Byungsun Hwang, Soohyun Kim, Youngghyu Sun and Jinyoung Kim ()
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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

Energies, 2024, vol. 17, issue 3, 1-24

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.

Keywords: energy management systems; 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; machine learning (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
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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