The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
DeepComfort: Energy-Efficient Thermal Comfort Control in Buildings Via Reinforcement Learning
Open accessAbstract
Heating, ventilation, and air conditioning (HVAC) are extremely energy consuming, accounting for 40% of total building energy consumption. It is crucial to design some energy-efficient building thermal comfort control strategy which can reduce the energy consumption of the HVAC while maintaining the comfort of the occupants. However, implementing such a strategy is challenging, because the changes of the thermal states in a building environment are influenced by various factors. The relationships among these influencing factors are hard to model and are always different in different building environments. To address this challenge, we propose a deep-reinforcement-learning-based framework, DeepComfort, for thermal comfort control in buildings. We formulate the thermal comfort control as a cost-minimization problem by jointly considering the energy consumption of the HVAC and the occupants' thermal comfort. We first design a deep feedforward neural network (FNN)-based approach for predicting the occupants' thermal comfort and then propose a deep deterministic policy gradients (DDPGs)-based approach for learning the optimal thermal comfort control policy. We implement a building thermal comfort control simulation environment and evaluate the performance under various settings. The experimental results show that our approaches can improve the performance of thermal comfort prediction by 14.5% and reduce the energy consumption of HVAC by 4.31% while improving the occupants' thermal comfort by 13.6%.
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Cites
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Cited by
Intelligent multi-zone residential HVAC control strategy based on deep reinforcement learning
Du, Y., Zandi, H., Kotevska, O., Kurte, K., Munk, J., Amasyali, K., Mckee, E., & Li, F. (2021) · Applied Energy · doi:10.1016/j.apenergy.2020.116117 · Cited by 268
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
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Metadata
- Authors
- Gao, G., Li, J., & Wen, Y.
- Venue
- IEEE Internet of Things Journal
- Published
- 2020-09
- Volume
- 7
- Issue
- 9
- Pages
- 8472-8484
- ISSN
- 2327-4662 (ISSN-L)
- 2372-2541
- OpenAlex
- W3023669592
- Cited by
- 283
- References
- 37
- FWCI
- 17.96
- Type
- journal article
- Language
- en
- Open access
- green
- Licence
- Funders
- Project Fund from DSAIR@NTU, and a BSEWWT Project Fund from National Research Foundation Singapore, administrated through the BSEWWT Program Office (BSEWWT2017_2_06)
- Green Buildings Innovation Cluster (NRF2015ENC-GBICRD001-012)
- Building and Construction Authority Singapore
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- Gao, G., Li, J., & Wen, Y. (2020). DeepComfort: Energy-Efficient Thermal Comfort Control in Buildings Via Reinforcement Learning. IEEE Internet of Things Journal. https://doi.org/10.1109/jiot.2020.2992117
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