The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
Intelligent multi-zone residential HVAC control strategy based on deep reinforcement learning
Open accessAbstract
Abstract Residential heating, ventilation, and air conditioning (HVAC) has been considered as an important demand response resource. However, the optimization of residential HVAC control is no trivial task due to the complexity of the thermal dynamic models of buildings and uncertainty associated with both occupant-driven heat loads and weather forecasts. In this paper, we apply a novel model-free deep reinforcement learning (RL) method, known as the deep deterministic policy gradient (DDPG), to generate an optimal control strategy for a multi-zone residential HVAC system with the goal of minimizing energy consumption cost while maintaining the users’ comfort. The applied deep RL-based method learns through continuous interaction with a simulated building environment and without referring to any prior model knowledge. Simulation results show that compared with the state-of-art deep Q network (DQN), the DDPG-based HVAC control strategy can reduce the energy consumption cost by 15% and reduce the comfort violation by 79%; and when compared with a rule-based HVAC control strategy, the comfort violation can be reduced by 98%. In addition, experiments with different building models and retail price models demonstrate that the well-trained DDPG-based HVAC control strategy has high generalization and adaptability to unseen environments, which indicates its practicability for real-world implementation.
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Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357
Mastering the game of Go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612
Continuous control with deep reinforcement learning
Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., & Wierstra, D. (2015) · arXiv · doi:10.48550/arxiv.1509.02971 · Cited by 10,782
A review on buildings energy consumption information
Pérez-Lombard, L., Ortiz, J., & Pout, C. (2008) · Energy and Buildings · doi:10.1016/j.enbuild.2007.03.007 · Cited by 6,649
An Evaluation of the HVAC Load Potential for Providing Load Balancing Service
Lu, N. (2012) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2012.2183649 · Cited by 739
On-Line Building Energy Optimization Using Deep Reinforcement Learning
Mocanu, E., Mocanu, D. C., Nguyen, P. H., Liotta, A., Webber, M. E., Gibescu, M., & Slootweg, J. G. (2019) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2018.2834219 · Cited by 649
Building operation and energy performance: Monitoring, analysis and optimisation toolkit
Costa, A., Keane, M. M., Torrens, J. I., & Corry, E. (2013) · Applied Energy · doi:10.1016/j.apenergy.2011.10.037 · Cited by 394
Du, Y., & Li, F. (2020) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2019.2930299 · Cited by 393
Self-citationMulti-Agent Deep Reinforcement Learning for HVAC Control in Commercial Buildings
Yu, L., Sun, Y., Xu, Z., Shen, C., Yue, D., Jiang, T., & Guan, X. (2021) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2020.3011739 · Cited by 353
Zhang, Z., Chong, A., Pan, Y., Zhang, C., & Lam, K. P. (2019) · Energy and Buildings · doi:10.1016/j.enbuild.2019.07.029 · Cited by 339
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An overview of machine learning applications for smart buildings
Alanne, K., & Sierla, S. (2022) · Sustainable Cities and Society · doi:10.1016/j.scs.2021.103445 · Cited by 280
Saleem, M., Shakir, M., Usman, M., Bajwa, M., Shabbir, N., Shams Ghahfarokhi, P., & Daniel, K. (2023) · Energies · doi:10.3390/en16124835 · Cited by 197
Reinforcement learning for HVAC control in intelligent buildings: A technical and conceptual review
Al Sayed, K., Boodi, A., Sadeghian Broujeny, R., & Beddiar, K. (2024) · Journal of Building Engineering · doi:10.1016/j.jobe.2024.110085 · Cited by 140
Adesanya, M. A., Obasekore, H., Rabiu, A., Na, W.-H., Ogunlowo, Q. O., Akpenpuun, T. D., Kim, M.-H., Kim, H.-T., Kang, B.-Y., & Lee, H.-W. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124126 · Cited by 72
Metadata
- Authors
- Du, Y., Zandi, H., Kotevska, O., Kurte, K., Munk, J., Amasyali, K., Mckee, E., & Li, F.
- Venue
- Applied Energy
- Publisher
- Elsevier BV
- Published
- 2021-01
- Volume
- 281
- Pages
- 116117
- ISSN
- 0306-2619 (ISSN-L)
- OpenAlex
- W3095810316
- Cited by
- 268
- References
- 28
- FWCI
- 17.96
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Du, Y., Zandi, H., Kotevska, O., Kurte, K., Munk, J., Amasyali, K., Mckee, E., & Li, F. (2021). Intelligent multi-zone residential HVAC control strategy based on deep reinforcement learning. Applied Energy. https://doi.org/10.1016/j.apenergy.2020.116117
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