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Applications of reinforcement learning in energy systems

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Cited by:

  1. Ahmad, Tanveer & Madonski, Rafal & Zhang, Dongdong & Huang, Chao & Mujeeb, Asad, 2022. "Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
  2. Mohammad Mahdi Forootan & Iman Larki & Rahim Zahedi & Abolfazl Ahmadi, 2022. "Machine Learning and Deep Learning in Energy Systems: A Review," Sustainability, MDPI, vol. 14(8), pages 1-49, April.
  3. Nik, Vahid M. & Hosseini, Mohammad, 2023. "CIRLEM: a synergic integration of Collective Intelligence and Reinforcement learning in Energy Management for enhanced climate resilience and lightweight computation," Applied Energy, Elsevier, vol. 350(C).
  4. Yao, Ganzhou & Luo, Zirong & Lu, Zhongyue & Wang, Mangkuan & Shang, Jianzhong & Guerrerob, Josep M., 2023. "Unlocking the potential of wave energy conversion: A comprehensive evaluation of advanced maximum power point tracking techniques and hybrid strategies for sustainable energy harvesting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 185(C).
  5. Ajagekar, Akshay & You, Fengqi, 2022. "Quantum computing and quantum artificial intelligence for renewable and sustainable energy: A emerging prospect towards climate neutrality," Renewable and Sustainable Energy Reviews, Elsevier, vol. 165(C).
  6. Ayas Shaqour & Aya Hagishima, 2022. "Systematic Review on Deep Reinforcement Learning-Based Energy Management for Different Building Types," Energies, MDPI, vol. 15(22), pages 1-27, November.
  7. Amir Ali Safaei Pirooz & Mohammad J. Sanjari & Young-Jin Kim & Stuart Moore & Richard Turner & Wayne W. Weaver & Dipti Srinivasan & Josep M. Guerrero & Mohammad Shahidehpour, 2023. "Adaptation of High Spatio-Temporal Resolution Weather/Load Forecast in Real-World Distributed Energy-System Operation," Energies, MDPI, vol. 16(8), pages 1-16, April.
  8. Caputo, Cesare & Cardin, Michel-Alexandre & Ge, Pudong & Teng, Fei & Korre, Anna & Antonio del Rio Chanona, Ehecatl, 2023. "Design and planning of flexible mobile Micro-Grids using Deep Reinforcement Learning," Applied Energy, Elsevier, vol. 335(C).
  9. Qiu, Dawei & Wang, Yi & Hua, Weiqi & Strbac, Goran, 2023. "Reinforcement learning for electric vehicle applications in power systems:A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
  10. Ode Bokker & Henning Schlachter & Vanessa Beutel & Stefan Geißendörfer & Karsten von Maydell, 2022. "Reactive Power Control of a Converter in a Hardware-Based Environment Using Deep Reinforcement Learning," Energies, MDPI, vol. 16(1), pages 1-12, December.
  11. Ahmed Abdelaziz & Vitor Santos & Miguel Sales Dias, 2021. "Machine Learning Techniques in the Energy Consumption of Buildings: A Systematic Literature Review Using Text Mining and Bibliometric Analysis," Energies, MDPI, vol. 14(22), pages 1-31, November.
  12. Qiu, Dawei & Wang, Yi & Zhang, Tingqi & Sun, Mingyang & Strbac, Goran, 2023. "Hierarchical multi-agent reinforcement learning for repair crews dispatch control towards multi-energy microgrid resilience," Applied Energy, Elsevier, vol. 336(C).
  13. Zhu, Ziqing & Hu, Ze & Chan, Ka Wing & Bu, Siqi & Zhou, Bin & Xia, Shiwei, 2023. "Reinforcement learning in deregulated energy market: A comprehensive review," Applied Energy, Elsevier, vol. 329(C).
  14. 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).
  15. Hao, Zhaojun & Di Maio, Francesco & Zio, Enrico, 2023. "A sequential decision problem formulation and deep reinforcement learning solution of the optimization of O&M of cyber-physical energy systems (CPESs) for reliable and safe power production and supply," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
  16. Biemann, Marco & Scheller, Fabian & Liu, Xiufeng & Huang, Lizhen, 2021. "Experimental evaluation of model-free reinforcement learning algorithms for continuous HVAC control," Applied Energy, Elsevier, vol. 298(C).
  17. Luca Pinciroli & Piero Baraldi & Guido Ballabio & Michele Compare & Enrico Zio, 2021. "Deep Reinforcement Learning Based on Proximal Policy Optimization for the Maintenance of a Wind Farm with Multiple Crews," Energies, MDPI, vol. 14(20), pages 1-17, October.
  18. Li, Yanxue & Wang, Zixuan & Xu, Wenya & Gao, Weijun & Xu, Yang & Xiao, Fu, 2023. "Modeling and energy dynamic control for a ZEH via hybrid model-based deep reinforcement learning," Energy, Elsevier, vol. 277(C).
  19. Barja-Martinez, Sara & Aragüés-Peñalba, Mònica & Munné-Collado, Íngrid & Lloret-Gallego, Pau & Bullich-Massagué, Eduard & Villafafila-Robles, Roberto, 2021. "Artificial intelligence techniques for enabling Big Data services in distribution networks: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
  20. Jendoubi, Imen & Bouffard, François, 2023. "Multi-agent hierarchical reinforcement learning for energy management," Applied Energy, Elsevier, vol. 332(C).
  21. Zhong, Shengyuan & Wang, Xiaoyuan & Zhao, Jun & Li, Wenjia & Li, Hao & Wang, Yongzhen & Deng, Shuai & Zhu, Jiebei, 2021. "Deep reinforcement learning framework for dynamic pricing demand response of regenerative electric heating," Applied Energy, Elsevier, vol. 288(C).
  22. Han, Kunlun & Yang, Kai & Yin, Linfei, 2022. "Lightweight actor-critic generative adversarial networks for real-time smart generation control of microgrids," Applied Energy, Elsevier, vol. 317(C).
  23. Harrold, Daniel J.B. & Cao, Jun & Fan, Zhong, 2022. "Data-driven battery operation for energy arbitrage using rainbow deep reinforcement learning," Energy, Elsevier, vol. 238(PC).
  24. Blad, C. & Bøgh, S. & Kallesøe, C. & Raftery, Paul, 2023. "A laboratory test of an Offline-trained Multi-Agent Reinforcement Learning Algorithm for Heating Systems," Applied Energy, Elsevier, vol. 337(C).
  25. Jianxun Luo & Wei Zhang & Hui Wang & Wenmiao Wei & Jinpeng He, 2023. "Research on Data-Driven Optimal Scheduling of Power System," Energies, MDPI, vol. 16(6), pages 1-15, March.
  26. Bio Gassi, Karim & Baysal, Mustafa, 2023. "Improving real-time energy decision-making model with an actor-critic agent in modern microgrids with energy storage devices," Energy, Elsevier, vol. 263(PE).
  27. Soleimanzade, Mohammad Amin & Kumar, Amit & Sadrzadeh, Mohtada, 2022. "Novel data-driven energy management of a hybrid photovoltaic-reverse osmosis desalination system using deep reinforcement learning," Applied Energy, Elsevier, vol. 317(C).
  28. Omar Al-Ani & Sanjoy Das, 2022. "Reinforcement Learning: Theory and Applications in HEMS," Energies, MDPI, vol. 15(17), pages 1-37, September.
  29. Zhu, Dafeng & Yang, Bo & Liu, Yuxiang & Wang, Zhaojian & Ma, Kai & Guan, Xinping, 2022. "Energy management based on multi-agent deep reinforcement learning for a multi-energy industrial park," Applied Energy, Elsevier, vol. 311(C).
  30. Homod, Raad Z. & Togun, Hussein & Kadhim Hussein, Ahmed & Noraldeen Al-Mousawi, Fadhel & Yaseen, Zaher Mundher & Al-Kouz, Wael & Abd, Haider J. & Alawi, Omer A. & Goodarzi, Marjan & Hussein, Omar A., 2022. "Dynamics analysis of a novel hybrid deep clustering for unsupervised learning by reinforcement of multi-agent to energy saving in intelligent buildings," Applied Energy, Elsevier, vol. 313(C).
  31. Ifaei, Pouya & Nazari-Heris, Morteza & Tayerani Charmchi, Amir Saman & Asadi, Somayeh & Yoo, ChangKyoo, 2023. "Sustainable energies and machine learning: An organized review of recent applications and challenges," Energy, Elsevier, vol. 266(C).
  32. Sun, Qingkai & Wang, Xiaojun & Liu, Zhao & Mirsaeidi, Sohrab & He, Jinghan & Pei, Wei, 2022. "Multi-agent energy management optimization for integrated energy systems under the energy and carbon co-trading market," Applied Energy, Elsevier, vol. 324(C).
  33. Perera, A.T.D. & Zhao, Bingyu & Wang, Zhe & Soga, Kenichi & Hong, Tianzhen, 2023. "Optimal design of microgrids to improve wildfire resilience for vulnerable communities at the wildland-urban interface," Applied Energy, Elsevier, vol. 335(C).
  34. Muqing Wu & Qingsu He & Yuping Liu & Ziqiang Zhang & Zhongwen Shi & Yifan He, 2022. "Machine Learning Techniques for Decarbonizing and Managing Renewable Energy Grids," Sustainability, MDPI, vol. 14(21), pages 1-13, October.
  35. Kirstin Beyer & Robert Beckmann & Stefan Geißendörfer & Karsten von Maydell & Carsten Agert, 2021. "Adaptive Online-Learning Volt-Var Control for Smart Inverters Using Deep Reinforcement Learning," Energies, MDPI, vol. 14(7), pages 1-11, April.
  36. Nebiyu Kedir & Phuong H. D. Nguyen & Citlaly Pérez & Pedro Ponce & Aminah Robinson Fayek, 2023. "Systematic Literature Review on Fuzzy Hybrid Methods in Photovoltaic Solar Energy: Opportunities, Challenges, and Guidance for Implementation," Energies, MDPI, vol. 16(9), pages 1-38, April.
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