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Intelligent multi-zone residential HVAC control strategy based on deep reinforcement learning
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- 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).
- 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).
- Qin, Haosen & Meng, Tao & Chen, Kan & Li, Zhengwei, 2024. "A comparative study of DQN and D3QN for HVAC system optimization control," Energy, Elsevier, vol. 307(C).
- Wang, Hao & Chen, Xiwen & Vital, Natan & Duffy, Edward & Razi, Abolfazl, 2024. "Energy optimization for HVAC systems in multi-VAV open offices: A deep reinforcement learning approach," Applied Energy, Elsevier, vol. 356(C).
- Homod, Raad Z. & Mohammed, Hayder Ibrahim & Abderrahmane, Aissa & Alawi, Omer A. & Khalaf, Osamah Ibrahim & Mahdi, Jasim M. & Guedri, Kamel & Dhaidan, Nabeel S. & Albahri, A.S. & Sadeq, Abdellatif M. , 2023. "Deep clustering of Lagrangian trajectory for multi-task learning to energy saving in intelligent buildings using cooperative multi-agent," Applied Energy, Elsevier, vol. 351(C).
- DeQuante Rashon Mckoy & Raymond Charles Tesiero & Yaa Takyiwaa Acquaah & Balakrishna Gokaraju, 2023. "Review of HVAC Systems History and Future Applications," Energies, MDPI, vol. 16(17), pages 1-15, August.
- 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).
- 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).
- 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).
- Omar Al-Ani & Sanjoy Das, 2022. "Reinforcement Learning: Theory and Applications in HEMS," Energies, MDPI, vol. 15(17), pages 1-37, September.
- Panagiotis Michailidis & Iakovos Michailidis & Dimitrios Vamvakas & Elias Kosmatopoulos, 2023. "Model-Free HVAC Control in Buildings: A Review," Energies, MDPI, vol. 16(20), pages 1-45, October.
- 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).
- Zhuang, Dian & Gan, Vincent J.L. & Duygu Tekler, Zeynep & Chong, Adrian & Tian, Shuai & Shi, Xing, 2023. "Data-driven predictive control for smart HVAC system in IoT-integrated buildings with time-series forecasting and reinforcement learning," Applied Energy, Elsevier, vol. 338(C).
- Razzano, Giuseppe & Brandi, Silvio & Piscitelli, Marco Savino & Capozzoli, Alfonso, 2025. "Rule extraction from deep reinforcement learning controller and comparative analysis with ASHRAE control sequences for the optimal management of Heating, Ventilation, and Air Conditioning (HVAC) syste," Applied Energy, Elsevier, vol. 381(C).
- 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).
- 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).
- Xinran Zeng & Chunhui Li & Xiaoying Li & Chennan Mao & Zhengwei Li & Zhenhai Li, 2025. "Energy Efficiency Optimization of Air Conditioning Systems Towards Low-Carbon Cleanrooms: Review and Future Perspectives," Energies, MDPI, vol. 18(13), pages 1-37, July.
- Park, Jong-Whi & Ju, Young-Min & Kim, You-Gwon & Kim, Hak-Sung, 2023. "50% reduction in energy consumption in an actual cold storage facility using a deep reinforcement learning-based control algorithm," Applied Energy, Elsevier, vol. 352(C).
- Clara Ceccolini & Roozbeh Sangi, 2022. "Benchmarking Approaches for Assessing the Performance of Building Control Strategies: A Review," Energies, MDPI, vol. 15(4), pages 1-30, February.
- Yan, Biao & Yang, Wansheng & He, Fuquan & Huang, Kehua & Zeng, Wenhao & Zhang, Wenlong & Ye, Haiseng, 2022. "Strategical district cooling system operation in hub airport terminals, a research focusing on COVID-19 pandemic impact," Energy, Elsevier, vol. 255(C).
- Lu, Ruyuan & Li, Xin & Chen, Ronghao & Lei, Aimin & Ma, Xiaoming, 2024. "An Alternative Reinforcement Learning (ARL) control strategy for data center air-cooled HVAC systems," Energy, Elsevier, vol. 308(C).
- 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).
- 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).
- 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).
- Zeyue Sun & Mohsen Eskandari & Chaoran Zheng & Ming Li, 2022. "Handling Computation Hardness and Time Complexity Issue of Battery Energy Storage Scheduling in Microgrids by Deep Reinforcement Learning," Energies, MDPI, vol. 16(1), pages 1-20, December.
- 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).
- Song, Yuguang & Xia, Mingchao & Chen, Qifang & Chen, Fangjian, 2023. "A data-model fusion dispatch strategy for the building energy flexibility based on the digital twin," Applied Energy, Elsevier, vol. 332(C).
- Sinha, Anshuman & Thakkar, Harshul & Rezaei, Fateme & Kawajiri, Yoshiaki & Realff, Matthew J., 2022. "Reduced building energy consumption by combined indoor CO2 and H2O composition control," Applied Energy, Elsevier, vol. 322(C).
- 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).
- He, Xianya & Huang, Jingzhi & Liu, Zekun & Lin, Jian & Jing, Rui & Zhao, Yingru, 2023. "Topology optimization of thermally activated building system in high-rise building," Energy, Elsevier, vol. 284(C).
- Dimitrios Vamvakas & Panagiotis Michailidis & Christos Korkas & Elias Kosmatopoulos, 2023. "Review and Evaluation of Reinforcement Learning Frameworks on Smart Grid Applications," Energies, MDPI, vol. 16(14), pages 1-38, July.
- Coraci, Davide & Brandi, Silvio & Hong, Tianzhen & Capozzoli, Alfonso, 2023. "Online transfer learning strategy for enhancing the scalability and deployment of deep reinforcement learning control in smart buildings," Applied Energy, Elsevier, vol. 333(C).
- Zhou, Xinlei & Du, Han & Xue, Shan & Ma, Zhenjun, 2024. "Recent advances in data mining and machine learning for enhanced building energy management," Energy, Elsevier, vol. 307(C).
- Zhang, Bin & Hu, Weihao & Ghias, Amer M.Y.M. & Xu, Xiao & Chen, Zhe, 2022. "Multi-agent deep reinforcement learning-based coordination control for grid-aware multi-buildings," Applied Energy, Elsevier, vol. 328(C).
- Song, Yuguang & Chen, Fangjian & Xia, Mingchao & Chen, Qifang, 2022. "The interactive dispatch strategy for thermostatically controlled loads based on the source–load collaborative evolution," Applied Energy, Elsevier, vol. 309(C).
- Wang, Qiaochu & Ding, Yan & Kong, Xiangfei & Tian, Zhe & Xu, Linrui & He, Qing, 2022. "Load pattern recognition based optimization method for energy flexibility in office buildings," Energy, Elsevier, vol. 254(PC).
- Charalampos Rafail Lazaridis & Iakovos Michailidis & Georgios Karatzinis & Panagiotis Michailidis & Elias Kosmatopoulos, 2024. "Evaluating Reinforcement Learning Algorithms in Residential Energy Saving and Comfort Management," Energies, MDPI, vol. 17(3), pages 1-33, January.
- Silvestri, Alberto & Coraci, Davide & Brandi, Silvio & Capozzoli, Alfonso & Borkowski, Esther & Köhler, Johannes & Wu, Duan & Zeilinger, Melanie N. & Schlueter, Arno, 2024. "Real building implementation of a deep reinforcement learning controller to enhance energy efficiency and indoor temperature control," Applied Energy, Elsevier, vol. 368(C).
- M. Usman Saleem & Mustafa Shakir & M. Rehan Usman & M. Hamza Tahir Bajwa & Noman Shabbir & Payam Shams Ghahfarokhi & Kamran Daniel, 2023. "Integrating Smart Energy Management System with Internet of Things and Cloud Computing for Efficient Demand Side Management in Smart Grids," Energies, MDPI, vol. 16(12), pages 1-21, June.
- Zhou, Xinlei & Lin, Wenye & Kumar, Ritunesh & Cui, Ping & Ma, Zhenjun, 2022. "A data-driven strategy using long short term memory models and reinforcement learning to predict building electricity consumption," Applied Energy, Elsevier, vol. 306(PB).
- Mudhafar Al-Saadi & Maher Al-Greer & Michael Short, 2023. "Reinforcement Learning-Based Intelligent Control Strategies for Optimal Power Management in Advanced Power Distribution Systems: A Survey," Energies, MDPI, vol. 16(4), pages 1-38, February.
- 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).
- 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.
- Dasheng Lee & Liyuan Chen, 2022. "Sustainable Air-Conditioning Systems Enabled by Artificial Intelligence: Research Status, Enterprise Patent Analysis, and Future Prospects," Sustainability, MDPI, vol. 14(12), pages 1-82, June.
- Cui, Can & Xue, Jiahui & Liu, Lanjun, 2025. "Optimal control of HVAC systems through active disturbance rejection control-assisted reinforcement learning," Energy, Elsevier, vol. 323(C).