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Reinforcement learning for demand response: A review of algorithms and modeling techniques

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Cited by:

  1. 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.
  2. 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).
  3. Kim, Sunwoo & Choi, Yechan & Park, Joungho & Adams, Derrick & Heo, Seongmin & Lee, Jay H., 2024. "Multi-period, multi-timescale stochastic optimization model for simultaneous capacity investment and energy management decisions for hybrid Micro-Grids with green hydrogen production under uncertainty," Renewable and Sustainable Energy Reviews, Elsevier, vol. 190(PA).
  4. 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).
  5. Jiang, Yuliang & Zhu, Shanying & Xu, Qimin & Yang, Bo & Guan, Xinping, 2023. "Hybrid modeling-based temperature and humidity adaptive control for a multi-zone HVAC system," Applied Energy, Elsevier, vol. 334(C).
  6. 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.
  7. Gokhale, Gargya & Claessens, Bert & Develder, Chris, 2022. "Physics informed neural networks for control oriented thermal modeling of buildings," Applied Energy, Elsevier, vol. 314(C).
  8. Eduardo J. Salazar & Mauro Jurado & Mauricio E. Samper, 2023. "Reinforcement Learning-Based Pricing and Incentive Strategy for Demand Response in Smart Grids," Energies, MDPI, vol. 16(3), pages 1-33, February.
  9. Magdalena Krystyna Wyrwicka & Ewa Więcek-Janka & Łukasz Brzeziński, 2023. "Transition to Sustainable Energy System for Smart Cities—Literature Review," Energies, MDPI, vol. 16(21), pages 1-26, October.
  10. 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).
  11. 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).
  12. Touzani, Samir & Prakash, Anand Krishnan & Wang, Zhe & Agarwal, Shreya & Pritoni, Marco & Kiran, Mariam & Brown, Richard & Granderson, Jessica, 2021. "Controlling distributed energy resources via deep reinforcement learning for load flexibility and energy efficiency," Applied Energy, Elsevier, vol. 304(C).
  13. Pavlos S. Georgilakis, 2020. "Review of Computational Intelligence Methods for Local Energy Markets at the Power Distribution Level to Facilitate the Integration of Distributed Energy Resources: State-of-the-art and Future Researc," Energies, MDPI, vol. 13(1), pages 1-37, January.
  14. Jin, Ruiyang & Zhou, Yuke & Lu, Chao & Song, Jie, 2022. "Deep reinforcement learning-based strategy for charging station participating in demand response," Applied Energy, Elsevier, vol. 328(C).
  15. Kou, Peng & Liang, Deliang & Wang, Chen & Wu, Zihao & Gao, Lin, 2020. "Safe deep reinforcement learning-based constrained optimal control scheme for active distribution networks," Applied Energy, Elsevier, vol. 264(C).
  16. Aya Amer & Khaled Shaban & Ahmed Massoud, 2022. "Demand Response in HEMSs Using DRL and the Impact of Its Various Configurations and Environmental Changes," Energies, MDPI, vol. 15(21), pages 1-20, November.
  17. Ma, Nan & Aviv, Dorit & Guo, Hongshan & Braham, William W., 2021. "Measuring the right factors: A review of variables and models for thermal comfort and indoor air quality," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
  18. Tsaousoglou, Georgios & Giraldo, Juan S. & Paterakis, Nikolaos G., 2022. "Market Mechanisms for Local Electricity Markets: A review of models, solution concepts and algorithmic techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
  19. Davide Fop & Ali Reza Yaghoubi & Alfonso Capozzoli, 2024. "Validation of a Model Predictive Control Strategy on a High Fidelity Building Emulator," Energies, MDPI, vol. 17(20), pages 1-20, October.
  20. Juan D. Velásquez & Lorena Cadavid & Carlos J. Franco, 2023. "Intelligence Techniques in Sustainable Energy: Analysis of a Decade of Advances," Energies, MDPI, vol. 16(19), pages 1-45, October.
  21. Prevedello, Giulio & Werth, Annette, 2021. "The benefits of sharing in off-grid microgrids: A case study in the Philippines," Applied Energy, Elsevier, vol. 303(C).
  22. Jingwen Yang & Ping Wang & Yongfeng Ju, 2024. "Variable Speed Limit Intelligent Decision-Making Control Strategy Based on Deep Reinforcement Learning under Emergencies," Sustainability, MDPI, vol. 16(3), pages 1-19, January.
  23. Chen, Zhiwen & Deng, Qiao & Ren, Hao & Zhao, Zhengrun & Peng, Tao & Yang, Chunhua & Gui, Weihua, 2022. "A new energy consumption prediction method for chillers based on GraphSAGE by combining empirical knowledge and operating data," Applied Energy, Elsevier, vol. 310(C).
  24. Pinto, Giuseppe & Kathirgamanathan, Anjukan & Mangina, Eleni & Finn, Donal P. & Capozzoli, Alfonso, 2022. "Enhancing energy management in grid-interactive buildings: A comparison among cooperative and coordinated architectures," Applied Energy, Elsevier, vol. 310(C).
  25. Svetozarevic, B. & Baumann, C. & Muntwiler, S. & Di Natale, L. & Zeilinger, M.N. & Heer, P., 2022. "Data-driven control of room temperature and bidirectional EV charging using deep reinforcement learning: Simulations and experiments," Applied Energy, Elsevier, vol. 307(C).
  26. Lin, Xinyou & Zeng, Songrong & Li, Xuefan, 2021. "Online correction predictive energy management strategy using the Q-learning based swarm optimization with fuzzy neural network," Energy, Elsevier, vol. 223(C).
  27. Van-Hai Bui & Akhtar Hussain & Hak-Man Kim, 2019. "Q-Learning-Based Operation Strategy for Community Battery Energy Storage System (CBESS) in Microgrid System," Energies, MDPI, vol. 12(9), pages 1-17, May.
  28. Gao, Yuan & Matsunami, Yuki & Miyata, Shohei & Akashi, Yasunori, 2022. "Operational optimization for off-grid renewable building energy system using deep reinforcement learning," Applied Energy, Elsevier, vol. 325(C).
  29. Pinto, Giuseppe & Deltetto, Davide & Capozzoli, Alfonso, 2021. "Data-driven district energy management with surrogate models and deep reinforcement learning," Applied Energy, Elsevier, vol. 304(C).
  30. Shi, Zhongtuo & Yao, Wei & Li, Zhouping & Zeng, Lingkang & Zhao, Yifan & Zhang, Runfeng & Tang, Yong & Wen, Jinyu, 2020. "Artificial intelligence techniques for stability analysis and control in smart grids: Methodologies, applications, challenges and future directions," Applied Energy, Elsevier, vol. 278(C).
  31. Huang, Pei & Fan, Cheng & Zhang, Xingxing & Wang, Jiayuan, 2019. "A hierarchical coordinated demand response control for buildings with improved performances at building group," Applied Energy, Elsevier, vol. 242(C), pages 684-694.
  32. Yin, Xin & Chen, Haoyong & Liang, Zipeng & Zhu, Yanjin, 2023. "A Flexibility-oriented robust transmission expansion planning approach under high renewable energy resource penetration," Applied Energy, Elsevier, vol. 351(C).
  33. Carol Vigurs & Chris Maidment & Michael Fell & David Shipworth, 2021. "Customer Privacy Concerns as a Barrier to Sharing Data about Energy Use in Smart Local Energy Systems: A Rapid Realist Review," Energies, MDPI, vol. 14(5), pages 1-33, February.
  34. Dorokhova, Marina & Martinson, Yann & Ballif, Christophe & Wyrsch, Nicolas, 2021. "Deep reinforcement learning control of electric vehicle charging in the presence of photovoltaic generation," Applied Energy, Elsevier, vol. 301(C).
  35. Antonopoulos, Ioannis & Robu, Valentin & Couraud, Benoit & Kirli, Desen & Norbu, Sonam & Kiprakis, Aristides & Flynn, David & Elizondo-Gonzalez, Sergio & Wattam, Steve, 2020. "Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
  36. Ye, Jin & Wang, Xianlian & Hua, Qingsong & Sun, Li, 2024. "Deep reinforcement learning based energy management of a hybrid electricity-heat-hydrogen energy system with demand response," Energy, Elsevier, vol. 305(C).
  37. Xie, Jiahan & Ajagekar, Akshay & You, Fengqi, 2023. "Multi-Agent attention-based deep reinforcement learning for demand response in grid-responsive buildings," Applied Energy, Elsevier, vol. 342(C).
  38. Abou-Foul, Mohamad & Ruiz-Alba, Jose L. & López-Tenorio, Pablo J., 2023. "The impact of artificial intelligence capabilities on servitization: The moderating role of absorptive capacity-A dynamic capabilities perspective," Journal of Business Research, Elsevier, vol. 157(C).
  39. Ruiqiu Yao & Yukun Hu & Liz Varga, 2023. "Applications of Agent-Based Methods in Multi-Energy Systems—A Systematic Literature Review," Energies, MDPI, vol. 16(5), pages 1-36, March.
  40. Ramya Kuppusamy & Srete Nikolovski & Yuvaraja Teekaraman, 2023. "Review of Machine Learning Techniques for Power Quality Performance Evaluation in Grid-Connected Systems," Sustainability, MDPI, vol. 15(20), pages 1-29, October.
  41. Jun Dong & A-Ru-Han Bao & Yao Liu & Xi-Hao Dou & Dong-Ran Liu & Gui-Yuan Xue, 2022. "Dynamic Differential Game Strategy of the Energy Big Data Ecosystem Considering Technological Innovation," Sustainability, MDPI, vol. 14(12), pages 1-24, June.
  42. Shen, Rendong & Zhong, Shengyuan & Wen, Xin & An, Qingsong & Zheng, Ruifan & Li, Yang & Zhao, Jun, 2022. "Multi-agent deep reinforcement learning optimization framework for building energy system with renewable energy," Applied Energy, Elsevier, vol. 312(C).
  43. Vašak, Mario & Banjac, Anita & Hure, Nikola & Novak, Hrvoje & Kovačević, Marko, 2023. "Predictive control based assessment of building demand flexibility in fixed time windows," Applied Energy, Elsevier, vol. 329(C).
  44. Sabarathinam Srinivasan & Suresh Kumarasamy & Zacharias E. Andreadakis & Pedro G. Lind, 2023. "Artificial Intelligence and Mathematical Models of Power Grids Driven by Renewable Energy Sources: A Survey," Energies, MDPI, vol. 16(14), pages 1-56, July.
  45. Fan, Lin & Su, Huai & Wang, Wei & Zio, Enrico & Zhang, Li & Yang, Zhaoming & Peng, Shiliang & Yu, Weichao & Zuo, Lili & Zhang, Jinjun, 2022. "A systematic method for the optimization of gas supply reliability in natural gas pipeline network based on Bayesian networks and deep reinforcement learning," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
  46. Han, Gwangwoo & Joo, Hong-Jin & Lim, Hee-Won & An, Young-Sub & Lee, Wang-Je & Lee, Kyoung-Ho, 2023. "Data-driven heat pump operation strategy using rainbow deep reinforcement learning for significant reduction of electricity cost," Energy, Elsevier, vol. 270(C).
  47. 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.
  48. Kinga Stecuła & Radosław Wolniak & Wieslaw Wes Grebski, 2023. "AI-Driven Urban Energy Solutions—From Individuals to Society: A Review," Energies, MDPI, vol. 16(24), pages 1-34, December.
  49. 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).
  50. 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).
  51. Bhuiyan, Erphan A. & Hossain, Md. Zahid & Muyeen, S.M. & Fahim, Shahriar Rahman & Sarker, Subrata K. & Das, Sajal K., 2021. "Towards next generation virtual power plant: Technology review and frameworks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
  52. Guo, Yurun & Wang, Shugang & Wang, Jihong & Zhang, Tengfei & Ma, Zhenjun & Jiang, Shuang, 2024. "Key district heating technologies for building energy flexibility: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
  53. Michael Bachseitz & Muhammad Sheryar & David Schmitt & Thorsten Summ & Christoph Trinkl & Wilfried Zörner, 2024. "PV-Optimized Heat Pump Control in Multi-Family Buildings Using a Reinforcement Learning Approach," Energies, MDPI, vol. 17(8), pages 1-16, April.
  54. Charbonnier, Flora & Peng, Bei & Vienne, Julie & Stai, Eleni & Morstyn, Thomas & McCulloch, Malcolm, 2025. "Centralised rehearsal of decentralised cooperation: Multi-agent reinforcement learning for the scalable coordination of residential energy flexibility," Applied Energy, Elsevier, vol. 377(PA).
  55. Sun, Fangyuan & Kong, Xiangyu & Wu, Jianzhong & Gao, Bixuan & Chen, Ke & Lu, Ning, 2022. "DSM pricing method based on A3C and LSTM under cloud-edge environment," Applied Energy, Elsevier, vol. 315(C).
  56. Oleh Lukianykhin & Tetiana Bogodorova, 2021. "Voltage Control-Based Ancillary Service Using Deep Reinforcement Learning," Energies, MDPI, vol. 14(8), pages 1-22, April.
  57. Park, Keonwoo & Moon, Ilkyeong, 2022. "Multi-agent deep reinforcement learning approach for EV charging scheduling in a smart grid," Applied Energy, Elsevier, vol. 328(C).
  58. Shafqat Jawad & Junyong Liu, 2020. "Electrical Vehicle Charging Services Planning and Operation with Interdependent Power Networks and Transportation Networks: A Review of the Current Scenario and Future Trends," Energies, MDPI, vol. 13(13), pages 1-24, July.
  59. Rocchetta, R. & Bellani, L. & Compare, M. & Zio, E. & Patelli, E., 2019. "A reinforcement learning framework for optimal operation and maintenance of power grids," Applied Energy, Elsevier, vol. 241(C), pages 291-301.
  60. Sun, Hongchang & Niu, Yanlei & Li, Chengdong & Zhou, Changgeng & Zhai, Wenwen & Chen, Zhe & Wu, Hao & Niu, Lanqiang, 2022. "Energy consumption optimization of building air conditioning system via combining the parallel temporal convolutional neural network and adaptive opposition-learning chimp algorithm," Energy, Elsevier, vol. 259(C).
  61. Carlos Cruz & Esther Palomar & Ignacio Bravo & Alfredo Gardel, 2020. "Cooperative Demand Response Framework for a Smart Community Targeting Renewables: Testbed Implementation and Performance Evaluation," Energies, MDPI, vol. 13(11), pages 1-20, June.
  62. Charbonnier, Flora & Morstyn, Thomas & McCulloch, Malcolm D., 2022. "Coordination of resources at the edge of the electricity grid: Systematic review and taxonomy," Applied Energy, Elsevier, vol. 318(C).
  63. Christian Blad & Simon Bøgh & Carsten Kallesøe, 2021. "A Multi-Agent Reinforcement Learning Approach to Price and Comfort Optimization in HVAC-Systems," Energies, MDPI, vol. 14(22), pages 1-20, November.
  64. Perera, A.T.D. & Kamalaruban, Parameswaran, 2021. "Applications of reinforcement learning in energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
  65. Wang, Yi & Qiu, Dawei & Strbac, Goran, 2022. "Multi-agent deep reinforcement learning for resilience-driven routing and scheduling of mobile energy storage systems," Applied Energy, Elsevier, vol. 310(C).
  66. Vladimir Franki & Darin Majnarić & Alfredo Višković, 2023. "A Comprehensive Review of Artificial Intelligence (AI) Companies in the Power Sector," Energies, MDPI, vol. 16(3), pages 1-35, January.
  67. Kanakadhurga, Dharmaraj & Prabaharan, Natarajan, 2022. "Demand side management in microgrid: A critical review of key issues and recent trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
  68. Paiho, Satu & Kiljander, Jussi & Sarala, Roope & Siikavirta, Hanne & Kilkki, Olli & Bajpai, Arpit & Duchon, Markus & Pahl, Marc-Oliver & Wüstrich, Lars & Lübben, Christian & Kirdan, Erkin & Schindler,, 2021. "Towards cross-commodity energy-sharing communities – A review of the market, regulatory, and technical situation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
  69. Liudong Chen & Bolun Xu, 2024. "Prudent Price-Responsive Demands," Papers 2405.16356, arXiv.org.
  70. Nik, Vahid M. & Moazami, Amin, 2021. "Using collective intelligence to enhance demand flexibility and climate resilience in urban areas," Applied Energy, Elsevier, vol. 281(C).
  71. Xiao, Boyi & Yang, Weiwei & Wu, Jiamin & Walker, Paul D. & Zhang, Nong, 2022. "Energy management strategy via maximum entropy reinforcement learning for an extended range logistics vehicle," Energy, Elsevier, vol. 253(C).
  72. 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.
  73. Tan, Yingqi & Xu, Jingyi & Ma, Junyi & Li, Zirui & Chen, Huiyan & Xi, Junqiang & Liu, Haiou, 2024. "A transferable perception-guided EMS for series hybrid electric unmanned tracked vehicles," Energy, Elsevier, vol. 306(C).
  74. Rajat Gupta & Sahar Zahiri & Johanna Morey, 2023. "Enhancing User Engagement in Local Energy Initiatives Using Smart Local Energy Engagement Tools: A Meta Study," Energies, MDPI, vol. 16(7), pages 1-25, March.
  75. Li, Ke & Ye, Ning & Li, Shuzhen & Wang, Haiyang & Zhang, Chenghui, 2023. "Distributed collaborative operation strategies in multi-agent integrated energy system considering integrated demand response based on game theory," Energy, Elsevier, vol. 273(C).
  76. Zhou, Jianhao & Xue, Siwu & Xue, Yuan & Liao, Yuhui & Liu, Jun & Zhao, Wanzhong, 2021. "A novel energy management strategy of hybrid electric vehicle via an improved TD3 deep reinforcement learning," Energy, Elsevier, vol. 224(C).
  77. Charbonnier, Flora & Morstyn, Thomas & McCulloch, Malcolm D., 2022. "Scalable multi-agent reinforcement learning for distributed control of residential energy flexibility," Applied Energy, Elsevier, vol. 314(C).
  78. Ajagekar, Akshay & Decardi-Nelson, Benjamin & You, Fengqi, 2024. "Energy management for demand response in networked greenhouses with multi-agent deep reinforcement learning," Applied Energy, Elsevier, vol. 355(C).
  79. Guo, Yuxiang & Qu, Shengli & Wang, Chuang & Xing, Ziwen & Duan, Kaiwen, 2024. "Optimal dynamic thermal management for data center via soft actor-critic algorithm with dynamic control interval and combined-value state space," Applied Energy, Elsevier, vol. 373(C).
  80. Zeng, Lanting & Qiu, Dawei & Sun, Mingyang, 2022. "Resilience enhancement of multi-agent reinforcement learning-based demand response against adversarial attacks," Applied Energy, Elsevier, vol. 324(C).
  81. Ibrahim, Muhammad Sohail & Dong, Wei & Yang, Qiang, 2020. "Machine learning driven smart electric power systems: Current trends and new perspectives," Applied Energy, Elsevier, vol. 272(C).
  82. Langer, Lissy & Volling, Thomas, 2022. "A reinforcement learning approach to home energy management for modulating heat pumps and photovoltaic systems," Applied Energy, Elsevier, vol. 327(C).
  83. Qiu, Dawei & Dong, Zihang & Zhang, Xi & Wang, Yi & Strbac, Goran, 2022. "Safe reinforcement learning for real-time automatic control in a smart energy-hub," Applied Energy, Elsevier, vol. 309(C).
  84. Tushar, Wayes & Yuen, Chau & Saha, Tapan K. & Morstyn, Thomas & Chapman, Archie C. & Alam, M. Jan E. & Hanif, Sarmad & Poor, H. Vincent, 2021. "Peer-to-peer energy systems for connected communities: A review of recent advances and emerging challenges," Applied Energy, Elsevier, vol. 282(PA).
  85. Amin, Amin & Kem, Oudom & Gallegos, Pablo & Chervet, Philipp & Ksontini, Feirouz & Mourshed, Monjur, 2022. "Demand response in buildings: Unlocking energy flexibility through district-level electro-thermal simulation," Applied Energy, Elsevier, vol. 305(C).
  86. Guo, Fangzhou & Ham, Sang woo & Kim, Donghun & Moon, Hyeun Jun, 2025. "Deep reinforcement learning control for co-optimizing energy consumption, thermal comfort, and indoor air quality in an office building," Applied Energy, Elsevier, vol. 377(PA).
  87. Ottavia Valentini & Nikoleta Andreadou & Paolo Bertoldi & Alexandre Lucas & Iolanda Saviuc & Evangelos Kotsakis, 2022. "Demand Response Impact Evaluation: A Review of Methods for Estimating the Customer Baseline Load," Energies, MDPI, vol. 15(14), pages 1-36, July.
  88. Omar Al-Ani & Sanjoy Das, 2022. "Reinforcement Learning: Theory and Applications in HEMS," Energies, MDPI, vol. 15(17), pages 1-37, September.
  89. Hernandez-Matheus, Alejandro & Löschenbrand, Markus & Berg, Kjersti & Fuchs, Ida & Aragüés-Peñalba, Mònica & Bullich-Massagué, Eduard & Sumper, Andreas, 2022. "A systematic review of machine learning techniques related to local energy communities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 170(C).
  90. 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).
  91. Wang, Zhe & Hong, Tianzhen, 2020. "Reinforcement learning for building controls: The opportunities and challenges," Applied Energy, Elsevier, vol. 269(C).
  92. Thibaut Th'eate & Antonio Sutera & Damien Ernst, 2023. "Matching of Everyday Power Supply and Demand with Dynamic Pricing: Problem Formalisation and Conceptual Analysis," Papers 2301.11587, arXiv.org.
  93. Zhang, Yiwen & Lin, Rui & Mei, Zhen & Lyu, Minghao & Jiang, Huaiguang & Xue, Ying & Zhang, Jun & Gao, David Wenzhong, 2024. "Interior-point policy optimization based multi-agent deep reinforcement learning method for secure home energy management under various uncertainties," Applied Energy, Elsevier, vol. 376(PA).
  94. 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).
  95. Anand Krishnan Prakash & Kun Zhang & Pranav Gupta & David Blum & Marc Marshall & Gabe Fierro & Peter Alstone & James Zoellick & Richard Brown & Marco Pritoni, 2020. "Solar+ Optimizer: A Model Predictive Control Optimization Platform for Grid Responsive Building Microgrids," Energies, MDPI, vol. 13(12), pages 1-27, June.
  96. 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).
  97. Lilia Tightiz & Joon Yoo, 2022. "A Review on a Data-Driven Microgrid Management System Integrating an Active Distribution Network: Challenges, Issues, and New Trends," Energies, MDPI, vol. 15(22), pages 1-24, November.
  98. Davide Deltetto & Davide Coraci & Giuseppe Pinto & Marco Savino Piscitelli & Alfonso Capozzoli, 2021. "Exploring the Potentialities of Deep Reinforcement Learning for Incentive-Based Demand Response in a Cluster of Small Commercial Buildings," Energies, MDPI, vol. 14(10), pages 1-25, May.
  99. Gong, Xun & Wang, Xiaozhe & Cao, Bo, 2023. "On data-driven modeling and control in modern power grids stability: Survey and perspective," Applied Energy, Elsevier, vol. 350(C).
  100. Ribó-Pérez, D. & Carrión, A. & Rodríguez García, J. & Álvarez Bel, C., 2021. "Ex-post evaluation of Interruptible Load programs with a system optimisation perspective," Applied Energy, Elsevier, vol. 303(C).
  101. Sapountzoglou, Nikolaos & Lago, Jesus & De Schutter, Bart & Raison, Bertrand, 2020. "A generalizable and sensor-independent deep learning method for fault detection and location in low-voltage distribution grids," Applied Energy, Elsevier, vol. 276(C).
  102. Yi Kuang & Xiuli Wang & Hongyang Zhao & Yijun Huang & Xianlong Chen & Xifan Wang, 2020. "Agent-Based Energy Sharing Mechanism Using Deep Deterministic Policy Gradient Algorithm," Energies, MDPI, vol. 13(19), pages 1-20, September.
  103. Ajagekar, Akshay & You, Fengqi, 2024. "Variational quantum circuit based demand response in buildings leveraging a hybrid quantum-classical strategy," Applied Energy, Elsevier, vol. 364(C).
  104. Miriam Benedetti & Francesca Bonfà & Vito Introna & Annalisa Santolamazza & Stefano Ubertini, 2019. "Real Time Energy Performance Control for Industrial Compressed Air Systems: Methodology and Applications," Energies, MDPI, vol. 12(20), pages 1-28, October.
  105. 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).
  106. Zheng, Lingwei & Wu, Hao & Guo, Siqi & Sun, Xinyu, 2023. "Real-time dispatch of an integrated energy system based on multi-stage reinforcement learning with an improved action-choosing strategy," Energy, Elsevier, vol. 277(C).
  107. Hindreen Rashid Abdulqadir & Adnan Mohsin Abdulazeez, 2021. "Reinforcement Learning and Modeling Techniques: A Review," International Journal of Science and Business, IJSAB International, vol. 5(3), pages 174-189.
  108. Liu, Shuo & Liu, Xiaohua & Zhang, Tao & Wang, Chaoliang & Liu, Wei, 2024. "Joint optimization for temperature and humidity independent control system based on multi-agent reinforcement learning with cooperative mechanisms," Applied Energy, Elsevier, vol. 375(C).
  109. Lu, Renzhi & Li, Yi-Chang & Li, Yuting & Jiang, Junhui & Ding, Yuemin, 2020. "Multi-agent deep reinforcement learning based demand response for discrete manufacturing systems energy management," Applied Energy, Elsevier, vol. 276(C).
  110. Pinto, Giuseppe & Piscitelli, Marco Savino & Vázquez-Canteli, José Ramón & Nagy, Zoltán & Capozzoli, Alfonso, 2021. "Coordinated energy management for a cluster of buildings through deep reinforcement learning," Energy, Elsevier, vol. 229(C).
  111. Langer, Lissy & Volling, Thomas, 2020. "An optimal home energy management system for modulating heat pumps and photovoltaic systems," Applied Energy, Elsevier, vol. 278(C).
  112. Máximo A. Domínguez-Garabitos & Víctor S. Ocaña-Guevara & Félix Santos-García & Adriana Arango-Manrique & Miguel Aybar-Mejía, 2022. "A Methodological Proposal for Implementing Demand-Shifting Strategies in the Wholesale Electricity Market," Energies, MDPI, vol. 15(4), pages 1-28, February.
  113. Kathirgamanathan, Anjukan & De Rosa, Mattia & Mangina, Eleni & Finn, Donal P., 2021. "Data-driven predictive control for unlocking building energy flexibility: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
  114. Zhang, Xiongfeng & Lu, Renzhi & Jiang, Junhui & Hong, Seung Ho & Song, Won Seok, 2021. "Testbed implementation of reinforcement learning-based demand response energy management system," Applied Energy, Elsevier, vol. 297(C).
  115. Cai, Jianyang & Yang, Haidong & Song, Cairong & Xu, Kangkang, 2024. "A novel graph convolutional network-based interpretable method for chiller energy consumption prediction considering the spatiotemporal coupling between variables," Energy, Elsevier, vol. 312(C).
  116. Pallonetto, Fabiano & De Rosa, Mattia & Milano, Federico & Finn, Donal P., 2019. "Demand response algorithms for smart-grid ready residential buildings using machine learning models," Applied Energy, Elsevier, vol. 239(C), pages 1265-1282.
  117. Zhang, Xiaohai & Ramírez-Mendiola, José Luis & Li, Mingtao & Guo, Liejin, 2022. "Electricity consumption pattern analysis beyond traditional clustering methods: A novel self-adapting semi-supervised clustering method and application case study," Applied Energy, Elsevier, vol. 308(C).
  118. Rae-Jun Park & Kyung-Bin Song & Bo-Sung Kwon, 2020. "Short-Term Load Forecasting Algorithm Using a Similar Day Selection Method Based on Reinforcement Learning," Energies, MDPI, vol. 13(10), pages 1-19, May.
  119. Perera, A.T.D. & Wickramasinghe, P.U. & Nik, Vahid M. & Scartezzini, Jean-Louis, 2020. "Introducing reinforcement learning to the energy system design process," Applied Energy, Elsevier, vol. 262(C).
  120. Zheng, Zhuang & Shafique, Muhammad & Luo, Xiaowei & Wang, Shengwei, 2024. "A systematic review towards integrative energy management of smart grids and urban energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
  121. Golmohamadi, Hessam, 2022. "Demand-side management in industrial sector: A review of heavy industries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
  122. 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).
  123. Xiang, Liu, 2020. "Energy emergency supply chain collaboration optimization with group consensus through reinforcement learning considering non-cooperative behaviours," Energy, Elsevier, vol. 210(C).
  124. Zhou, Jianhao & Liu, Jun & Xue, Yuan & Liao, Yuhui, 2022. "Total travel costs minimization strategy of a dual-stack fuel cell logistics truck enhanced with artificial potential field and deep reinforcement learning," Energy, Elsevier, vol. 239(PA).
  125. Lee, Kuan-Cheng & Yang, Hong-Tzer & Tang, Wenjun, 2022. "Data-driven online interactive bidding strategy for demand response," Applied Energy, Elsevier, vol. 319(C).
  126. Esmaeili Aliabadi, Danial & Chan, Katrina, 2022. "The emerging threat of artificial intelligence on competition in liberalized electricity markets: A deep Q-network approach," Applied Energy, Elsevier, vol. 325(C).
  127. Arroyo, Javier & Manna, Carlo & Spiessens, Fred & Helsen, Lieve, 2022. "Reinforced model predictive control (RL-MPC) for building energy management," Applied Energy, Elsevier, vol. 309(C).
  128. Christoforos Menos-Aikateriniadis & Ilias Lamprinos & Pavlos S. Georgilakis, 2022. "Particle Swarm Optimization in Residential Demand-Side Management: A Review on Scheduling and Control Algorithms for Demand Response Provision," Energies, MDPI, vol. 15(6), pages 1-26, March.
  129. Hu, Rong & Zhou, Kaile & Yin, Hui, 2024. "Reinforcement learning model for incentive-based integrated demand response considering demand-side coupling," Energy, Elsevier, vol. 308(C).
  130. Zeng, Huibin & Shao, Bilin & Dai, Hongbin & Tian, Ning & Zhao, Wei, 2023. "Incentive-based demand response strategies for natural gas considering carbon emissions and load volatility," Applied Energy, Elsevier, vol. 348(C).
  131. Philipp Wohlgenannt & Sebastian Hegenbart & Elias Eder & Mohan Kolhe & Peter Kepplinger, 2024. "Energy Demand Response in a Food-Processing Plant: A Deep Reinforcement Learning Approach," Energies, MDPI, vol. 17(24), pages 1-19, December.
  132. Kong, Xiangyu & Kong, Deqian & Yao, Jingtao & Bai, Linquan & Xiao, Jie, 2020. "Online pricing of demand response based on long short-term memory and reinforcement learning," Applied Energy, Elsevier, vol. 271(C).
  133. Chenhui Xu & Yunkai Huang, 2023. "Integrated Demand Response in Multi-Energy Microgrids: A Deep Reinforcement Learning-Based Approach," Energies, MDPI, vol. 16(12), pages 1-19, June.
  134. Couraud, Benoit & Andoni, Merlinda & Robu, Valentin & Norbu, Sonam & Chen, Si & Flynn, David, 2023. "Responsive FLEXibility: A smart local energy system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).
  135. Sun, Guoxin & Yu, Yongheng & Yu, Qihui & Tan, Xin & Wu, Linfeng & Qin, Ripeng & Wang, Yahui, 2024. "Enhanced temperature regulation in compound heating systems: Leveraging guided policy search and model predictive control," Renewable Energy, Elsevier, vol. 236(C).
  136. Haider, Rabab & Annaswamy, Anuradha M., 2022. "A hybrid architecture for volt-var control in active distribution grids," Applied Energy, Elsevier, vol. 312(C).
  137. Correa-Jullian, Camila & López Droguett, Enrique & Cardemil, José Miguel, 2020. "Operation scheduling in a solar thermal system: A reinforcement learning-based framework," Applied Energy, Elsevier, vol. 268(C).
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