The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
Deep Reinforcement Learning for Building HVAC Control
Abstract
Buildings account for nearly 40% of the total energy consumption in the United States, about half of which is used by the HVAC (heating, ventilation, and air conditioning) system. Intelligent scheduling of building HVAC systems has the potential to significantly reduce the energy cost. However, the traditional rule-based and model-based strategies are often inefficient in practice, due to the complexity in building thermal dynamics and heterogeneous environment disturbances. In this work, we develop a data-driven approach that leverages the deep reinforcement learning (DRL) technique, to intelligently learn the effective strategy for operating the building HVAC systems. We evaluate the performance of our DRL algorithm through simulations using the widely-adopted EnergyPlus tool. Experiments demonstrate that our DRL-based algorithm is more effective in energy cost reduction compared with the traditional rule-based approach, while maintaining the room temperature within desired range.
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Cites
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Cited by
Data-driven predictive control for unlocking building energy flexibility: A review
Kathirgamanathan, A., De Rosa, M., Mangina, E., & Finn, D. P. (2021) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.110120 · Cited by 321
Kazmi, H., Mehmood, F., Lodeweyckx, S., & Driesen, J. (2018) · Energy · doi:10.1016/j.energy.2017.12.019 · Cited by 109
Machine Learning for Smart and Energy-Efficient Buildings
Das, H. P., Lin, Y.-W., Agwan, U., Spangher, L., Devonport, A., Yang, Y., Drgoňa, J., Chong, A., Schiavon, S., & Spanos, C. J. (2024) · Environmental Data Science · doi:10.1017/eds.2023.43 · Cited by 78
Ekanayaka Gunasinghalge, L. U. G., Alazab, A., & Talukder, M. A. (2025) · Energy Informatics · doi:10.1186/s42162-025-00592-8 · Cited by 26
Metadata
- Authors
- Wei, T., Wang, Y., & Zhu, Q.
- Venue
- Proceedings of the 54th Annual Design Automation Conference 2017
- Publisher
- ACM
- Published
- 2017-06-18
- Pages
- 1-6
- ISSN
- 0738-100X (ISSN-L)
- OpenAlex
- W2625874945
- Cited by
- 478
- References
- 20
- FWCI
- 46.92
- Type
- conference paper
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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