Recent Trends and Issues of Energy Management Systems Using Machine Learning
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 literatur...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/17/3/624 |
| _version_ | 1797318806910009344 |
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| author | Seongwoo Lee Joonho Seon Byungsun Hwang Soohyun Kim Youngghyu Sun Jinyoung Kim |
| author_facet | Seongwoo Lee Joonho Seon Byungsun Hwang Soohyun Kim Youngghyu Sun Jinyoung Kim |
| author_sort | Seongwoo Lee |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T03:57:47Z |
| format | Article |
| id | doaj.art-7e6332e2e07c48958869aad52823bb34 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-08T03:57:47Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-7e6332e2e07c48958869aad52823bb342024-02-09T15:11:19ZengMDPI AGEnergies1996-10732024-01-0117362410.3390/en17030624Recent Trends and Issues of Energy Management Systems Using Machine LearningSeongwoo Lee0Joonho Seon1Byungsun Hwang2Soohyun Kim3Youngghyu Sun4Jinyoung Kim5Department of Electronic Convergence Engineering, Kwangwoon University, Seoul 01897, Republic of KoreaDepartment of Electronic Convergence Engineering, Kwangwoon University, Seoul 01897, Republic of KoreaDepartment of Electronic Convergence Engineering, Kwangwoon University, Seoul 01897, Republic of KoreaDepartment of Electronic Convergence Engineering, Kwangwoon University, Seoul 01897, Republic of KoreaDepartment of Electronic Convergence Engineering, Kwangwoon University, Seoul 01897, Republic of KoreaDepartment of Electronic Convergence Engineering, Kwangwoon University, Seoul 01897, Republic of KoreaEnergy 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.https://www.mdpi.com/1996-1073/17/3/624energy management systemsdistributed energy resourcesenergy management information systemsenergy storage systemsenergy trading risk management systemsdemand side management systems |
| spellingShingle | Seongwoo Lee Joonho Seon Byungsun Hwang Soohyun Kim Youngghyu Sun Jinyoung Kim Recent Trends and Issues of Energy Management Systems Using Machine Learning Energies energy management systems distributed energy resources energy management information systems energy storage systems energy trading risk management systems demand side management systems |
| title | Recent Trends and Issues of Energy Management Systems Using Machine Learning |
| title_full | Recent Trends and Issues of Energy Management Systems Using Machine Learning |
| title_fullStr | Recent Trends and Issues of Energy Management Systems Using Machine Learning |
| title_full_unstemmed | Recent Trends and Issues of Energy Management Systems Using Machine Learning |
| title_short | Recent Trends and Issues of Energy Management Systems Using Machine Learning |
| title_sort | recent trends and issues of energy management systems using machine learning |
| topic | energy management systems distributed energy resources energy management information systems energy storage systems energy trading risk management systems demand side management systems |
| url | https://www.mdpi.com/1996-1073/17/3/624 |
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