The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.
Deep reinforcement learning to optimise indoor temperature control and heating energy consumption in buildings
Open accessAbstract
Abstract In this work, Deep Reinforcement Learning (DRL) is implemented to control the supply water temperature setpoint to terminal units of a heating system. The experiment was carried out for an office building in an integrated simulation environment. A sensitivity analysis is carried out on relevant hyperparameters to identify their optimal configuration. Moreover, two sets of input variables were considered for assessing their impact on the adaptability capabilities of the DRL controller. In this context a static and dynamic deployment of the DRL controller is performed. The trained control agent is tested for four different scenarios to determine its adaptability to the variation of forcing variables such as weather conditions, occupant presence patterns and different indoor temperature setpoint requirements. The performance of the agent is evaluated against a reference controller that implements a combination of rule-based and climatic-based logics. As a result, when the set of variables are adequately selected a heating energy saving ranging between 5 and 12% is obtained with an enhanced indoor temperature control with both static and dynamic deployment. Eventually the study proves that if the set of input variables are not carefully selected a dynamic deployment is strictly required for obtaining good performance.
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Cites
Human-level control through deep reinforcement learning
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EnergyPlus エネルギー・シミュレーション・プログラム
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Cited by
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
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
Evaluation of advanced control strategies for building energy systems
Stoffel, P., Maier, L., Kümpel, A., Schreiber, T., & Müller, D. (2023) · Energy and Buildings · doi:10.1016/j.enbuild.2022.112709 · Cited by 126
“Another study on using RL for BES energy management applications was published by Brandi et al. ([72]).”
Coordinated energy management for a cluster of buildings through deep reinforcement learning
Pinto, G., Piscitelli, M. S., Vázquez-Canteli, J. R., Nagy, Z., & Capozzoli, A. (2021) · Energy · doi:10.1016/j.energy.2021.120725 · Cited by 108
Self-citationAdesanya, 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
- Brandi, S., Piscitelli, M. S., Martellacci, M., & Capozzoli, A.
- Venue
- Energy and Buildings
- Publisher
- Elsevier BV
- Published
- 2020-10
- Volume
- 224
- Pages
- 110225
- ISSN
- 0378-7788 (ISSN-L)
- OpenAlex
- W3034748593
- Cited by
- 258
- References
- 42
- FWCI
- 15.23
- Type
- journal article
- Language
- en
- Open access
- green
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