IDEAS home Printed from https://ideas.repec.org/r/eee/appene/v309y2022ics0306261921015932.html
   My bibliography  Save this item

Reinforced model predictive control (RL-MPC) for building energy management

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Ding, Yan & Zhang, Haozheng & Yang, Xiaochen & Tian, Zhe & Huang, Chen, 2024. "An adaptive switching control model for air conditioning systems based on information completeness," Applied Energy, Elsevier, vol. 375(C).
  2. 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.
  3. Elinor Ginzburg-Ganz & Itay Segev & Alexander Balabanov & Elior Segev & Sivan Kaully Naveh & Ram Machlev & Juri Belikov & Liran Katzir & Sarah Keren & Yoash Levron, 2024. "Reinforcement Learning Model-Based and Model-Free Paradigms for Optimal Control Problems in Power Systems: Comprehensive Review and Future Directions," Energies, MDPI, vol. 17(21), pages 1-54, October.
  4. Tian, Xiaoyu & Zhang, Hanwen & Liu, Lin & Huang, Jiahao & Liu, Liru & Liu, Jing, 2024. "Establishment of LCZ-based urban building energy consumption dataset in hot and humid subtropical regions through a bottom-up method," Applied Energy, Elsevier, vol. 368(C).
  5. Hu, Zehuan & Gao, Yuan & Sun, Luning & Mae, Masayuki & Imaizumi, Taiji, 2024. "Improved robust model predictive control for residential building air conditioning and photovoltaic power generation with battery energy storage system under weather forecast uncertainty," Applied Energy, Elsevier, vol. 371(C).
  6. Fu, Yangyang & Xu, Shichao & Zhu, Qi & O’Neill, Zheng & Adetola, Veronica, 2023. "How good are learning-based control v.s. model-based control for load shifting? Investigations on a single zone building energy system," Energy, Elsevier, vol. 273(C).
  7. Jian Tang & Hao Tian & Tianzheng Wang, 2024. "A Review of Model Predictive Control for the Municipal Solid Waste Incineration Process," Sustainability, MDPI, vol. 16(17), pages 1-35, September.
  8. Gao, Yuan & Matsunami, Yuki & Miyata, Shohei & Akashi, Yasunori, 2022. "Multi-agent reinforcement learning dealing with hybrid action spaces: A case study for off-grid oriented renewable building energy system," Applied Energy, Elsevier, vol. 326(C).
  9. Joanna Piotrowska-Woroniak & Krzysztof Cieśliński & Grzegorz Woroniak & Jonas Bielskus, 2022. "The Impact of Thermo-Modernization and Forecast Regulation on the Reduction of Thermal Energy Consumption and Reduction of Pollutant Emissions into the Atmosphere on the Example of Prefabricated Build," Energies, MDPI, vol. 15(8), pages 1-32, April.
  10. Wang, Xuezheng & Dong, Bing, 2024. "Long-term experimental evaluation and comparison of advanced controls for HVAC systems," Applied Energy, Elsevier, vol. 371(C).
  11. Gao, Yuan & Miyata, Shohei & Akashi, Yasunori, 2023. "Energy saving and indoor temperature control for an office building using tube-based robust model predictive control," Applied Energy, Elsevier, vol. 341(C).
  12. Zhao, Jing & Yang, Zilan & Shi, Linyu & Liu, Dehan & Li, Haonan & Mi, Yumiao & Wang, Hongbin & Feng, Meili & Hutagaol, Timothy Joseph, 2024. "Photovoltaic capacity dynamic tracking model predictive control strategy of air-conditioning systems with consideration of flexible loads," Applied Energy, Elsevier, vol. 356(C).
  13. Patrick Wilk & Ning Wang & Jie Li, 2024. "Multi-Agent Reinforcement Learning for Smart Community Energy Management," Energies, MDPI, vol. 17(20), pages 1-21, October.
  14. 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).
  15. Wang, Xiaoyu & Jia, Hongjie & Jin, Xiaolong & Mu, Yunfei & Wei, Wei & Yu, Xiaodan & Liang, Shuo, 2024. "Bi-level optimal operations for grid operator and low-carbon building prosumers with peer-to-peer energy sharing," Applied Energy, Elsevier, vol. 359(C).
  16. 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.
  17. Gao, Renjing & Zhou, Guangli & Wang, Qi, 2024. "Real-time three-level energy management strategy for series hybrid wheel loaders based on WG-MPC," Energy, Elsevier, vol. 295(C).
  18. Lei, Yue & Zhan, Sicheng & Ono, Eikichi & Peng, Yuzhen & Zhang, Zhiang & Hasama, Takamasa & Chong, Adrian, 2022. "A practical deep reinforcement learning framework for multivariate occupant-centric control in buildings," Applied Energy, Elsevier, vol. 324(C).
  19. Zhou, Xinlei & Xue, Shan & Du, Han & Ma, Zhenjun, 2023. "Optimization of building demand flexibility using reinforcement learning and rule-based expert systems," Applied Energy, Elsevier, vol. 350(C).
  20. Nweye, Kingsley & Sankaranarayanan, Siva & Nagy, Zoltan, 2023. "MERLIN: Multi-agent offline and transfer learning for occupant-centric operation of grid-interactive communities," Applied Energy, Elsevier, vol. 346(C).
  21. Wang, Zixuan & Xiao, Fu & Ran, Yi & Li, Yanxue & Xu, Yang, 2024. "Scalable energy management approach of residential hybrid energy system using multi-agent deep reinforcement learning," Applied Energy, Elsevier, vol. 367(C).
  22. Rémy Rigo-Mariani & Alim Yakub, 2024. "Decision Tree Variations and Online Tuning for Real-Time Control of a Building in a Two-Stage Management Strategy," Energies, MDPI, vol. 17(11), pages 1-17, June.
  23. Amal Azzi & Mohamed Tabaa & Badr Chegari & Hanaa Hachimi, 2024. "Balancing Sustainability and Comfort: A Holistic Study of Building Control Strategies That Meet the Global Standards for Efficiency and Thermal Comfort," Sustainability, MDPI, vol. 16(5), pages 1-36, March.
  24. Luna, José Diogo Forte de Oliveira & Naspolini, Amir & Reis, Guilherme Nascimento Gouvêa dos & Mendes, Paulo Renato da Costa & Normey-Rico, Julio Elias, 2024. "A novel joint energy and demand management system for smart houses based on model predictive control, hybrid storage system and quality of experience concepts," Applied Energy, Elsevier, vol. 369(C).
  25. Xiaohan Fang & Peng Hong & Shuping He & Yuhao Zhang & Di Tan, 2024. "Multi-Layer Energy Management and Strategy Learning for Microgrids: A Proximal Policy Optimization Approach," Energies, MDPI, vol. 17(16), pages 1-22, August.
  26. Panagiotis Michailidis & Iakovos Michailidis & Socratis Gkelios & Elias Kosmatopoulos, 2024. "Artificial Neural Network Applications for Energy Management in Buildings: Current Trends and Future Directions," Energies, MDPI, vol. 17(3), pages 1-47, January.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.