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Incentive-based demand response for smart grid with reinforcement learning and deep neural network

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  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. Tsao, Yu-Chung & Thanh, Vo-Van & Lu, Jye-Chyi, 2021. "Sustainable advanced distribution management system design considering differential pricing schemes and carbon emissions," Energy, Elsevier, vol. 219(C).
  3. Mir Sayed Shah Danish, 2023. "AI and Expert Insights for Sustainable Energy Future," Energies, MDPI, vol. 16(8), pages 1-27, April.
  4. Dan Zhou & Xiaodie Niu & Yuzhe Xie & Peng Li & Jiandi Fang & Fanghong Guo, 2022. "An Economic Dispatch Method of Microgrid Based on Fully Distributed ADMM Considering Demand Response," Sustainability, MDPI, vol. 14(7), pages 1-17, March.
  5. Ivana Damjanović & Ivica Pavić & Mate Puljiz & Mario Brcic, 2022. "Deep Reinforcement Learning-Based Approach for Autonomous Power Flow Control Using Only Topology Changes," Energies, MDPI, vol. 15(19), pages 1-16, September.
  6. 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).
  7. Rajabi, Amin & Eskandari, Mohsen & Ghadi, Mojtaba Jabbari & Li, Li & Zhang, Jiangfeng & Siano, Pierluigi, 2020. "A comparative study of clustering techniques for electrical load pattern segmentation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 120(C).
  8. Godiana Hagile Philipo & Josephine Nakato Kakande & Stefan Krauter, 2022. "Neural Network-Based Demand-Side Management in a Stand-Alone Solar PV-Battery Microgrid Using Load-Shifting and Peak-Clipping," Energies, MDPI, vol. 15(14), pages 1-18, July.
  9. Zheng, Shunlin & Sun, Yi & Li, Bin & Qi, Bing & Zhang, Xudong & Li, Fei, 2021. "Incentive-based integrated demand response for multiple energy carriers under complex uncertainties and double coupling effects," Applied Energy, Elsevier, vol. 283(C).
  10. Xie, Guangrui & Chen, Xi & Weng, Yang, 2021. "Enhance load forecastability: Optimize data sampling policy by reinforcing user behaviors," European Journal of Operational Research, Elsevier, vol. 295(3), pages 924-934.
  11. Chao-Chung Hsu & Bi-Hai Jiang & Chun-Cheng Lin, 2023. "A Survey on Recent Applications of Artificial Intelligence and Optimization for Smart Grids in Smart Manufacturing," Energies, MDPI, vol. 16(22), pages 1-15, November.
  12. Lu, Renzhi & Bai, Ruichang & Huang, Yuan & Li, Yuting & Jiang, Junhui & Ding, Yuemin, 2021. "Data-driven real-time price-based demand response for industrial facilities energy management," Applied Energy, Elsevier, vol. 283(C).
  13. Isaías Gomes & Karol Bot & Maria Graça Ruano & António Ruano, 2022. "Recent Techniques Used in Home Energy Management Systems: A Review," Energies, MDPI, vol. 15(8), pages 1-41, April.
  14. Yueqiang Xu & Petri Ahokangas & Jean-Nicolas Louis & Eva Pongrácz, 2019. "Electricity Market Empowered by Artificial Intelligence: A Platform Approach," Energies, MDPI, vol. 12(21), pages 1-21, October.
  15. 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).
  16. Xu, Fangyuan & Zhu, Weidong & Wang, Yi Fei & Lai, Chun Sing & Yuan, Haoliang & Zhao, Yujia & Guo, Siming & Fu, Zhengxin, 2022. "A new deregulated demand response scheme for load over-shifting city in regulated power market," Applied Energy, Elsevier, vol. 311(C).
  17. Ma, Siyu & Liu, Hui & Wang, Ni & Huang, Lidong & Goh, Hui Hwang, 2023. "Incentive-based demand response under incomplete information based on the deep deterministic policy gradient," Applied Energy, Elsevier, vol. 351(C).
  18. Wenying Li & Ming Tang & Xinzhen Zhang & Danhui Gao & Jian Wang, 2021. "Operation of Distributed Battery Considering Demand Response Using Deep Reinforcement Learning in Grid Edge Control," Energies, MDPI, vol. 14(22), pages 1-18, November.
  19. 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).
  20. Rasheed, Muhammad Babar & R-Moreno, María D., 2022. "Minimizing pricing policies based on user load profiles and residential demand responses in smart grids," Applied Energy, Elsevier, vol. 310(C).
  21. 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).
  22. Hwang, Hyunkyeong & Yoon, Ahyun & Yoon, Yongtae & Moon, Seungil, 2023. "Demand response of HVAC systems for hosting capacity improvement in distribution networks: A comprehensive review and case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 187(C).
  23. Gholami, M. & Sanjari, M.J., 2021. "Multiobjective energy management in battery-integrated home energy systems," Renewable Energy, Elsevier, vol. 177(C), pages 967-975.
  24. Dominique Barth & Benjamin Cohen-Boulakia & Wilfried Ehounou, 2022. "Distributed Reinforcement Learning for the Management of a Smart Grid Interconnecting Independent Prosumers," Energies, MDPI, vol. 15(4), pages 1-19, February.
  25. Xu, Jiuping & Liu, Tingting, 2020. "Technological paradigm-based approaches towards challenges and policy shifts for sustainable wind energy development," Energy Policy, Elsevier, vol. 142(C).
  26. Qi, Chunyang & Song, Chuanxue & Xiao, Feng & Song, Shixin, 2022. "Generalization ability of hybrid electric vehicle energy management strategy based on reinforcement learning method," Energy, Elsevier, vol. 250(C).
  27. Zhanle Wang & Raman Paranjape & Zhikun Chen & Kai Zeng, 2019. "Multi-Agent Optimization for Residential Demand Response under Real-Time Pricing," Energies, MDPI, vol. 12(15), pages 1-15, July.
  28. 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.
  29. 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).
  30. Li, Zening & Su, Su & Jin, Xiaolong & Chen, Houhe, 2021. "Distributed energy management for active distribution network considering aggregated office buildings," Renewable Energy, Elsevier, vol. 180(C), pages 1073-1087.
  31. Tsoumalis, Georgios I. & Bampos, Zafeirios N. & Biskas, Pandelis N. & Keranidis, Stratos D. & Symeonidis, Polychronis A. & Voulgarakis, Dimitrios K., 2022. "A novel system for providing explicit demand response from domestic natural gas boilers," Applied Energy, Elsevier, vol. 317(C).
  32. 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).
  33. Lee, Kuan-Cheng & Yang, Hong-Tzer & Tang, Wenjun, 2022. "Data-driven online interactive bidding strategy for demand response," Applied Energy, Elsevier, vol. 319(C).
  34. 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).
  35. Dinh, Huy Truong & Lee, Kyu-haeng & Kim, Daehee, 2022. "Supervised-learning-based hour-ahead demand response for a behavior-based home energy management system approximating MILP optimization," Applied Energy, Elsevier, vol. 321(C).
  36. Abdollah Younesi & Hossein Shayeghi & Pierluigi Siano, 2020. "Assessing the Use of Reinforcement Learning for Integrated Voltage/Frequency Control in AC Microgrids," Energies, MDPI, vol. 13(5), pages 1-22, March.
  37. Kalim Ullah & Sajjad Ali & Taimoor Ahmad Khan & Imran Khan & Sadaqat Jan & Ibrar Ali Shah & Ghulam Hafeez, 2020. "An Optimal Energy Optimization Strategy for Smart Grid Integrated with Renewable Energy Sources and Demand Response Programs," Energies, MDPI, vol. 13(21), pages 1-17, November.
  38. 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.
  39. Ming, Hao & Meng, Jing & Gao, Ciwei & Song, Meng & Chen, Tao & Choi, Dae-Hyun, 2023. "Efficiency improvement of decentralized incentive-based demand response: Social welfare analysis and market mechanism design," Applied Energy, Elsevier, vol. 331(C).
  40. 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.
  41. 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).
  42. Allal-Chérif, Oihab & Simón-Moya, Virginia & Ballester, Antonio Carlos Cuenca, 2021. "Intelligent purchasing: How artificial intelligence can redefine the purchasing function," Journal of Business Research, Elsevier, vol. 124(C), pages 69-76.
  43. Yang, Lijun & Jiang, Yaning & Chong, Zhenxiao, 2023. "Optimal scheduling of electro-thermal system considering refined demand response and source-load-storage cooperative hydrogen production," Renewable Energy, Elsevier, vol. 215(C).
  44. Zhou, Huan & Fan, Shuai & Wu, Qing & Dong, Lianxin & Li, Zuyi & He, Guangyu, 2021. "Stimulus-response control strategy based on autonomous decentralized system theory for exploitation of flexibility by virtual power plant," Applied Energy, Elsevier, vol. 285(C).
  45. Seppo Sierla & Heikki Ihasalo & Valeriy Vyatkin, 2022. "A Review of Reinforcement Learning Applications to Control of Heating, Ventilation and Air Conditioning Systems," Energies, MDPI, vol. 15(10), pages 1-25, May.
  46. 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).
  47. Tostado-Véliz, Marcos & Kamel, Salah & Aymen, Flah & Jurado, Francisco, 2022. "A novel hybrid lexicographic-IGDT methodology for robust multi-objective solution of home energy management systems," Energy, Elsevier, vol. 253(C).
  48. 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).
  49. Ussama Assad & Muhammad Arshad Shehzad Hassan & Umar Farooq & Asif Kabir & Muhammad Zeeshan Khan & S. Sabahat H. Bukhari & Zain ul Abidin Jaffri & Judit Oláh & József Popp, 2022. "Smart Grid, Demand Response and Optimization: A Critical Review of Computational Methods," Energies, MDPI, vol. 15(6), pages 1-36, March.
  50. Lu, Renzhi & Bai, Ruichang & Ding, Yuemin & Wei, Min & Jiang, Junhui & Sun, Mingyang & Xiao, Feng & Zhang, Hai-Tao, 2021. "A hybrid deep learning-based online energy management scheme for industrial microgrid," Applied Energy, Elsevier, vol. 304(C).
  51. Grace Muriithi & Sunetra Chowdhury, 2021. "Optimal Energy Management of a Grid-Tied Solar PV-Battery Microgrid: A Reinforcement Learning Approach," Energies, MDPI, vol. 14(9), pages 1-24, May.
  52. 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).
  53. Haiteng Han & Chen Wu & Zhinong Wei & Haixiang Zang & Guoqiang Sun & Kang Sun & Tiantian Wei, 2021. "A Two-Stage Cooperative Dispatch Model for Power Systems Considering Security and Source-Load Interaction," Sustainability, MDPI, vol. 13(23), pages 1-18, December.
  54. Hlalele, Thabo G. & Zhang, Jiangfeng & Naidoo, Raj M. & Bansal, Ramesh C., 2021. "Multi-objective economic dispatch with residential demand response programme under renewable obligation," Energy, Elsevier, vol. 218(C).
  55. Gao, Jianwei & Ma, Zeyang & Guo, Fengjia, 2019. "The influence of demand response on wind-integrated power system considering participation of the demand side," Energy, Elsevier, vol. 178(C), pages 723-738.
  56. 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).
  57. Zhou, Kaile & Peng, Ning & Yin, Hui & Hu, Rong, 2023. "Urban virtual power plant operation optimization with incentive-based demand response," Energy, Elsevier, vol. 282(C).
  58. Zubair Khalid & Ghulam Abbas & Muhammad Awais & Thamer Alquthami & Muhammad Babar Rasheed, 2020. "A Novel Load Scheduling Mechanism Using Artificial Neural Network Based Customer Profiles in Smart Grid," Energies, MDPI, vol. 13(5), pages 1-23, February.
  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. 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.
  61. Cheng, Haoyuan & Lu, Tianguang & Hao, Ran & Li, Jiamei & Ai, Qian, 2024. "Incentive-based demand response optimization method based on federated learning with a focus on user privacy protection," Applied Energy, Elsevier, vol. 358(C).
  62. Perera, A.T.D. & Kamalaruban, Parameswaran, 2021. "Applications of reinforcement learning in energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
  63. Paraskevas Koukaras & Paschalis Gkaidatzis & Napoleon Bezas & Tommaso Bragatto & Federico Carere & Francesca Santori & Marcel Antal & Dimosthenis Ioannidis & Christos Tjortjis & Dimitrios Tzovaras, 2021. "A Tri-Layer Optimization Framework for Day-Ahead Energy Scheduling Based on Cost and Discomfort Minimization," Energies, MDPI, vol. 14(12), pages 1-24, June.
  64. Qi, Chunyang & Zhu, Yiwen & Song, Chuanxue & Yan, Guangfu & Xiao, Feng & Da wang, & Zhang, Xu & Cao, Jingwei & Song, Shixin, 2022. "Hierarchical reinforcement learning based energy management strategy for hybrid electric vehicle," Energy, Elsevier, vol. 238(PA).
  65. Zhang, Yang & Yang, Qingyu & Li, Donghe & An, Dou, 2022. "A reinforcement and imitation learning method for pricing strategy of electricity retailer with customers’ flexibility," Applied Energy, Elsevier, vol. 323(C).
  66. Davarzani, Sima & Pisica, Ioana & Taylor, Gareth A. & Munisami, Kevin J., 2021. "Residential Demand Response Strategies and Applications in Active Distribution Network Management," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
  67. 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).
  68. 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).
  69. 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.
  70. Omar Al-Ani & Sanjoy Das, 2022. "Reinforcement Learning: Theory and Applications in HEMS," Energies, MDPI, vol. 15(17), pages 1-37, September.
  71. Wang, Chaoyang & Qiao, Yongqiang & Liu, Ming & Zhao, Yongliang & Yan, Junjie, 2020. "Enhancing peak shaving capability by optimizing reheat-steam temperature control of a double-reheat boiler," Applied Energy, Elsevier, vol. 260(C).
  72. Wu, Yuankai & Tan, Huachun & Peng, Jiankun & Zhang, Hailong & He, Hongwen, 2019. "Deep reinforcement learning of energy management with continuous control strategy and traffic information for a series-parallel plug-in hybrid electric bus," Applied Energy, Elsevier, vol. 247(C), pages 454-466.
  73. Chen, Yongbao & Zhang, Lixin & Xu, Peng & Di Gangi, Alessandra, 2021. "Electricity demand response schemes in China: Pilot study and future outlook," Energy, Elsevier, vol. 224(C).
  74. 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).
  75. Sana Iqbal & Mohammad Sarfraz & Mohammad Ayyub & Mohd Tariq & Ripon K. Chakrabortty & Michael J. Ryan & Basem Alamri, 2021. "A Comprehensive Review on Residential Demand Side Management Strategies in Smart Grid Environment," Sustainability, MDPI, vol. 13(13), pages 1, June.
  76. Aparna Kumari & Riya Kakkar & Rajesh Gupta & Smita Agrawal & Sudeep Tanwar & Fayez Alqahtani & Amr Tolba & Maria Simona Raboaca & Daniela Lucia Manea, 2023. "Blockchain-Driven Real-Time Incentive Approach for Energy Management System," Mathematics, MDPI, vol. 11(4), pages 1-17, February.
  77. Zhao, Liyuan & Yang, Ting & Li, Wei & Zomaya, Albert Y., 2022. "Deep reinforcement learning-based joint load scheduling for household multi-energy system," Applied Energy, Elsevier, vol. 324(C).
  78. 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.
  79. Amit Shewale & Anil Mokhade & Nitesh Funde & Neeraj Dhanraj Bokde, 2022. "A Survey of Efficient Demand-Side Management Techniques for the Residential Appliance Scheduling Problem in Smart Homes," Energies, MDPI, vol. 15(8), pages 1-34, April.
  80. Yongsheng Cao & Yongquan Wang, 2022. "Smart Carbon Emission Scheduling for Electric Vehicles via Reinforcement Learning under Carbon Peak Target," Sustainability, MDPI, vol. 14(19), pages 1-16, October.
  81. Xianhao Shen & Yexin Zhang & Decheng Wang, 2022. "Online Charging Strategy for Electric Vehicle Clusters Based on Multi-Agent Reinforcement Learning and Long–Short Memory Networks," Energies, MDPI, vol. 15(13), pages 1-14, June.
  82. 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).
  83. 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).
  84. 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.
  85. 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.
  86. Vo-Van Thanh & Wencong Su & Bin Wang, 2022. "Optimal DC Microgrid Operation with Model Predictive Control-Based Voltage-Dependent Demand Response and Optimal Battery Dispatch," Energies, MDPI, vol. 15(6), pages 1-19, March.
  87. Boza, Pal & Evgeniou, Theodoros, 2021. "Artificial intelligence to support the integration of variable renewable energy sources to the power system," Applied Energy, Elsevier, vol. 290(C).
  88. Razzak, Abdur & Islam, Md. Tariqul & Roy, Palash & Razzaque, Md. Abdur & Hassan, Md. Rafiul & Hassan, Mohammad Mehedi, 2024. "Leveraging Deep Q-Learning to maximize consumer quality of experience in smart grid," Energy, Elsevier, vol. 290(C).
  89. Guo, Chenyu & Wang, Xin & Zheng, Yihui & Zhang, Feng, 2022. "Real-time optimal energy management of microgrid with uncertainties based on deep reinforcement learning," Energy, Elsevier, vol. 238(PC).
  90. 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).
  91. Huakun Huang & Dingrong Dai & Longtao Guo & Sihui Xue & Huijun Wu, 2023. "AI and Big Data-Empowered Low-Carbon Buildings: Challenges and Prospects," Sustainability, MDPI, vol. 15(16), pages 1-21, August.
  92. 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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