Operational optimization for off-grid renewable building energy system using deep reinforcement learning

Citations

Cited by:

1. 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).
2. Kaabinejadian, Amirreza & Pozarlik, Artur & Acar, Canan, 2025. "A systematic review of predictive, optimization, and smart control strategies for hydrogen-based building heating systems," Applied Energy, Elsevier, vol. 379(C).
3. 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.
4. Liang, Xinbin & Liu, Zhuoxuan & Wang, Jie & Jin, Xinqiao & Du, Zhimin, 2023. "Uncertainty quantification-based robust deep learning for building energy systems considering distribution shift problem," Applied Energy, Elsevier, vol. 337(C).
5. Gao, Yuan & Hu, Zehuan & Yamate, Shun & Otomo, Junichiro & Chen, Wei-An & Liu, Mingzhe & Xu, Tingting & Ruan, Yingjun & Shang, Juan, 2025. "Unlocking predictive insights and interpretability in deep reinforcement learning for Building-Integrated Photovoltaic and Battery (BIPVB) systems," Applied Energy, Elsevier, vol. 384(C).
6. Haikui Jin & Jian Wang & Ying Wang & Yingjun Ruan & Yuan Gao & Fanyue Qian & Xiaoyan Xu & Chen Ju & Xun Dong, 2025. "Adaptability Study of Hydrogen Fuel Cell Integrated Energy Systems," Energies, MDPI, vol. 18(8), pages 1-20, April.
7. 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).
8. Jia, Bin & Li, Fan & Sun, Bo, 2024. "Knowledge-network-embedded deep reinforcement learning: An innovative way to high-efficiently develop an energy management strategy for the integrated energy system with renewable energy sources and m," Energy, Elsevier, vol. 301(C).
9. Liu, Jiejie & Ma, Yanan & Chen, Ying & Zhao, Chunlu & Meng, Xianyang & Wu, Jiangtao, 2025. "Multi-agent deep reinforcement learning-based cooperative energy management for regional integrated energy system incorporating active demand-side management," Energy, Elsevier, vol. 319(C).
10. 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).
11. Lu, Yu & Xiang, Yue & Huang, Yuan & Yu, Bin & Weng, Liguo & Liu, Junyong, 2023. "Deep reinforcement learning based optimal scheduling of active distribution system considering distributed generation, energy storage and flexible load," Energy, Elsevier, vol. 271(C).
12. Liao, Chenxin & Miyata, Shohei & Qu, Ming & Akashi, Yasunori, 2025. "Year-round operational optimization of HVAC systems using hierarchical deep reinforcement learning for enhancing indoor air quality and reducing energy consumption," Applied Energy, Elsevier, vol. 390(C).
13. Nakıp, Mert & Çopur, Onur & Biyik, Emrah & Güzeliş, Cüneyt, 2023. "Renewable energy management in smart home environment via forecast embedded scheduling based on Recurrent Trend Predictive Neural Network," Applied Energy, Elsevier, vol. 340(C).
14. Gao, Yuan & Hu, Zehuan & Chen, Wei-An & Liu, Mingzhe & Ruan, Yingjun, 2025. "A revolutionary neural network architecture with interpretability and flexibility based on Kolmogorov–Arnold for solar radiation and temperature forecasting," Applied Energy, Elsevier, vol. 378(PA).
15. Keerthana Sivamayil & Elakkiya Rajasekar & Belqasem Aljafari & Srete Nikolovski & Subramaniyaswamy Vairavasundaram & Indragandhi Vairavasundaram, 2023. "A Systematic Study on Reinforcement Learning Based Applications," Energies, MDPI, vol. 16(3), pages 1-23, February.
16. Panagiotis Michailidis & Iakovos Michailidis & Elias Kosmatopoulos, 2025. "Reinforcement Learning for Optimizing Renewable Energy Utilization in Buildings: A Review on Applications and Innovations," Energies, MDPI, vol. 18(7), pages 1-40, March.
17. Yang, Sheng & Liu, Beilin & Li, Xiaolong & Liu, Zhiqiang & Liu, Yue & Xie, Nan & Ren, Jingzheng, 2023. "Flexibility index for a distributed energy system design optimization," Renewable Energy, Elsevier, vol. 219(P1).
18. Zhang, Yuhang & Zhang, Yi & Zheng, Bo & Cui, Hongzhi & Qi, He, 2025. "Statistical analysis for estimating the optimized battery capacity for roof-top PV energy system," Renewable Energy, Elsevier, vol. 242(C).
19. Ren, Peng & Dong, Yingchao & Zhang, Hongli & Wang, Jin & Fan, Xiaochao, 2025. "A unified robust planning framework for hydrogen energy multi-scale regulation of integrated energy system," Energy, Elsevier, vol. 314(C).
20. Ren, Kezheng & Liu, Jun & Wu, Zeyang & Liu, Xinglei & Nie, Yongxin & Xu, Haitao, 2024. "A data-driven DRL-based home energy management system optimization framework considering uncertain household parameters," Applied Energy, Elsevier, vol. 355(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. Deng, Xiangtian & Zhang, Yi & Jiang, Yi & Zhang, Yi & Qi, He, 2024. "A novel operation method for renewable building by combining distributed DC energy system and deep reinforcement learning," Applied Energy, Elsevier, vol. 353(PB).
23. Chen, Qi & Kuang, Zhonghong & Liu, Xiaohua & Zhang, Tao, 2024. "Application-oriented assessment of grid-connected PV-battery system with deep reinforcement learning in buildings considering electricity price dynamics," Applied Energy, Elsevier, vol. 364(C).
24. 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.
25. Gao, Yuan & Liu, Mingzhe & Hu, Zehuan & Yamate, Shun & Otomo, Junichiro & Chen, Wei-An & O’Neill, Zheng, 2025. "Quantitative analysis of energy justice in demand response: Insights from real residential data in Texas, USA," Renewable Energy, Elsevier, vol. 242(C).
26. Wenya Xu & Yanxue Li & Guanjie He & Yang Xu & Weijun Gao, 2023. "Performance Assessment and Comparative Analysis of Photovoltaic-Battery System Scheduling in an Existing Zero-Energy House Based on Reinforcement Learning Control," Energies, MDPI, vol. 16(13), pages 1-19, June.
27. Gao, Yuan & Hu, Zehuan & Chen, Wei-An & Liu, Mingzhe, 2024. "Solutions to the insufficiency of label data in renewable energy forecasting: A comparative and integrative analysis of domain adaptation and fine-tuning," Energy, Elsevier, vol. 302(C).
28. Gao, Yuan & Hu, Zehuan & Shi, Shanrui & Chen, Wei-An & Liu, Mingzhe, 2024. "Adversarial discriminative domain adaptation for solar radiation prediction: A cross-regional study for zero-label transfer learning in Japan," Applied Energy, Elsevier, vol. 359(C).