The DOI matches its Crossref record; 54 of the 54 references it cites were found in a registry or an index.
Whole building energy model for HVAC optimal control: A practical framework based on deep reinforcement learning
Abstract
Abstract Whole building energy model (BEM) is a physics-based modeling method for building energy simulation. It has been widely used in the building industry for code compliance, building design optimization, retrofit analysis, and other uses. Recent research also indicates its strong potential for the control of heating, ventilation and air-conditioning (HVAC) systems. However, its high-order nature and slow computational speed limit its practical application in real-time HVAC optimal control. Therefore, this study proposes a practical control framework (named BEM-DRL) that is based on deep reinforcement learning. The framework is implemented and assessed in a novel radiant heating system in an existing office building as a case study. The complete implementation process is presented in this study, including: building energy modeling for the novel heating system, multi-objective BEM calibration using the Bayesian method and the Genetic Algorithm, deep reinforcement learning training and simulation results evaluation, and control deployment. By analyzing the real-life control deployment data, it is found that BEM-DRL achieves 16.7% heating demand reduction with more than 95% probability compared to the old rule-based control. However, the framework still faces the practical challenges including building energy modeling of novel HVAC systems and multi-objective model calibration. Systematic study is also needed for the design of deep reinforcement learning training to provide a guideline for practitioners.
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Perera, A. T. D., & Kamalaruban, P. (2021) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.110618 · Cited by 420
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“…systems [Lacerda, 2017], power systems [Deng and Liu, 2018, Deng et al., 2020, Wang et al., 2019, Mello et al., 2020], building management [Zhang et al., 2019], traffic management [Jin and Ma, 2019], manufacturing [Govindaiah and Petty, 2019, Lepenioti et al., 2020, Dornheim and Link,…”
BackgroundData-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
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
Data mining in the construction industry: Present status, opportunities, and future trends
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Metadata
- Authors
- Zhang, Z., Chong, A., Pan, Y., Zhang, C., & Lam, K. P.
- Venue
- Energy and Buildings
- Publisher
- Elsevier BV
- Published
- 2019-09
- Volume
- 199
- Pages
- 472-490
- ISSN
- 0378-7788 (ISSN-L)
- OpenAlex
- W2957897705
- Cited by
- 339
- References
- 54
- FWCI
- 19.72
- Type
- journal article
- Language
- en
- Open access
- closed
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- Zhang, Z., Chong, A., Pan, Y., Zhang, C., & Lam, K. P. (2019). Whole building energy model for HVAC optimal control: A practical framework based on deep reinforcement learning. Energy and Buildings. https://doi.org/10.1016/j.enbuild.2019.07.029
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