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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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    1. Carlos Ruiz & Carlos M. Alaíz & José R. Dorronsoro, 2020. "Multitask Support Vector Regression for Solar and Wind Energy Prediction," Energies, MDPI, vol. 13(23), pages 1-21, November.
    2. Hua, Haochen & Qin, Yuchao & Hao, Chuantong & Cao, Junwei, 2019. "Optimal energy management strategies for energy Internet via deep reinforcement learning approach," Applied Energy, Elsevier, vol. 239(C), pages 598-609.
    3. Fontenot, Hannah & Dong, Bing, 2019. "Modeling and control of building-integrated microgrids for optimal energy management – A review," Applied Energy, Elsevier, vol. 254(C).
    4. Yujian Ye & Dawei Qiu & Huiyu Wang & Yi Tang & Goran Strbac, 2021. "Real-Time Autonomous Residential Demand Response Management Based on Twin Delayed Deep Deterministic Policy Gradient Learning," Energies, MDPI, vol. 14(3), pages 1-22, January.
    5. Jing, Rui & Wang, Meng & Zhang, Zhihui & Wang, Xiaonan & Li, Ning & Shah, Nilay & Zhao, Yingru, 2019. "Distributed or centralized? Designing district-level urban energy systems by a hierarchical approach considering demand uncertainties," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    6. Kakran, Sandeep & Chanana, Saurabh, 2018. "Smart operations of smart grids integrated with distributed generation: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 524-535.
    7. Lu, Renzhi & Hong, Seung Ho, 2019. "Incentive-based demand response for smart grid with reinforcement learning and deep neural network," Applied Energy, Elsevier, vol. 236(C), pages 937-949.
    8. Harrold, Daniel J.B. & Cao, Jun & Fan, Zhong, 2022. "Renewable energy integration and microgrid energy trading using multi-agent deep reinforcement learning," Applied Energy, Elsevier, vol. 318(C).
    9. S. Sofana Reka & Prakash Venugopal & V. Ravi & Tomislav Dragicevic, 2023. "Privacy-Based Demand Response Modeling for Residential Consumers Using Machine Learning with a Cloud–Fog-Based Smart Grid Environment," Energies, MDPI, vol. 16(4), pages 1-16, February.
    10. Wen, Lulu & Zhou, Kaile & Li, Jun & Wang, Shanyong, 2020. "Modified deep learning and reinforcement learning for an incentive-based demand response model," Energy, Elsevier, vol. 205(C).
    11. Vázquez-Canteli, José R. & Nagy, Zoltán, 2019. "Reinforcement learning for demand response: A review of algorithms and modeling techniques," Applied Energy, Elsevier, vol. 235(C), pages 1072-1089.
    12. Sharma, Pavitra & Dutt Mathur, Hitesh & Mishra, Puneet & Bansal, Ramesh C., 2022. "A critical and comparative review of energy management strategies for microgrids," Applied Energy, Elsevier, vol. 327(C).
    13. Arif Fikri Malek & Hazlie Mokhlis & Nurulafiqah Nadzirah Mansor & Jasrul Jamani Jamian & Li Wang & Munir Azam Muhammad, 2023. "Power Distribution System Outage Management Using Improved Resilience Metrics for Smart Grid Applications," Energies, MDPI, vol. 16(9), pages 1-21, May.
    14. Bhatti, Bilal Ahmad & Broadwater, Robert, 2019. "Energy trading in the distribution system using a non-model based game theoretic approach," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    15. Trinadh Pamulapati & Muhammed Cavus & Ishioma Odigwe & Adib Allahham & Sara Walker & Damian Giaouris, 2022. "A Review of Microgrid Energy Management Strategies from the Energy Trilemma Perspective," Energies, MDPI, vol. 16(1), pages 1-34, December.
    16. Khuram Shahzad & Sohail Iqbal & Hamid Mukhtar, 2021. "Optimal Fuzzy Energy Trading System in a Fog-Enabled Smart Grid," Energies, MDPI, vol. 14(4), pages 1-16, February.
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