Safe deep reinforcement learning for building energy management

Xiangwei Wang, Peng Wang, Renke Huang, Xiuli Zhu, Javier Arroyo and Ning Li

Applied Energy, 2025, vol. 377, issue PA, No S0306261924017112

Abstract: The optimization of building energy systems poses a complex challenge due to the dynamic nature of building environments and the need for ensuring both energy efficiency and system reliability. This paper proposes a novel approach that synergizes deep reinforcement learning (DRL) with model predictive control (MPC) for safe and efficient building energy management. The approach, named safe DRL, leverages the DRL to explore optimality in uncertain environments and the MPC to dynamically ensure constraints. Off-the-shelf DRL tools are utilized in the safe DRL approach which avoids case-by-case and effort-intensive hyperparameter tuning. MPC with only an approximate building model is then adopted to efficiently filter the DRL action candidates to ensure the safety of the constraints. Additionally, a comprehensive safety metric is proposed considering the duration and severity of constraint violation. Extensive case studies with high-fidelity building models show that the safe DRL approach significantly enhances the constraint satisfaction and improves building performance at the cost of slightly more decision time of a few seconds.

Keywords: Safe deep reinforcement learning; Model predictive control; Building energy management; Safety metric (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261924017112
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:377:y:2025:i:pa:s0306261924017112

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2024.124328

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().