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Article

Multi-Agent Deep Reinforcement Learning-Based HVAC and Electrochromic Window Control Framework

1
School of Electronic Science and Engineering (School of Microelectronics), South China Normal University, Foshan 528225, China
2
School of Artificial Intelligence, South China Normal University, Foshan 528225, China
*
Author to whom correspondence should be addressed.
Buildings 2025, 15(17), 3114; https://doi.org/10.3390/buildings15173114 (registering DOI)
Submission received: 1 August 2025 / Revised: 28 August 2025 / Accepted: 29 August 2025 / Published: 31 August 2025

Abstract

Deep reinforcement learning (DRL)-based HVAC control has shown clear advantages over rule-based and model predictive methods. However, most prior studies remain limited to HVAC-only optimization or simple coordination with operable windows. Such approaches do not adequately address buildings with fixed glazing systems—a common feature in high-rise offices—where the lack of operable windows restricts adaptive envelope interaction. To address this gap, this study proposes a multi-zone control framework that integrates HVAC systems with electrochromic windows (ECWs). The framework leverages the Q-value Mixing (QMIX) algorithm to dynamically coordinate ECW transmittance with HVAC setpoints, aiming to enhance energy efficiency and thermal comfort, particularly in high-consumption buildings such as offices. Its performance is evaluated using EnergyPlus simulations. The results show that the proposed approach reduces HVAC energy use by 19.8% compared to the DQN-based HVAC-only control and by 40.28% relative to conventional rule-based control (RBC). In comparison with leading multi-agent deep reinforcement learning (MADRL) algorithms, including MADQN, VDN, and MAPPO, the framework reduces HVAC energy consumption by 1–5% and maintains a thermal comfort violation rate (TCVR) of less than 1% with an average temperature variation of 0.35 C Moreover, the model demonstrates strong generalizability, achieving 16.58–58.12% energy savings across six distinct climatic regions—ranging from tropical (Singapore) to temperate (Beijing)—with up to 48.2% savings observed in Chengdu. Our framework indicates the potential of coordinating HVAC systems with ECWs in simulation, while also identifying limitations that need to be addressed for real-world deployment.
Keywords: building energy saving; multi-agent deep reinforcement learning; electrochromic window; control methods building energy saving; multi-agent deep reinforcement learning; electrochromic window; control methods

Share and Cite

MDPI and ACS Style

Chen, H.; Sun, D.; Sun, Y.; Zhang, Y.; Yang, H. Multi-Agent Deep Reinforcement Learning-Based HVAC and Electrochromic Window Control Framework. Buildings 2025, 15, 3114. https://doi.org/10.3390/buildings15173114

AMA Style

Chen H, Sun D, Sun Y, Zhang Y, Yang H. Multi-Agent Deep Reinforcement Learning-Based HVAC and Electrochromic Window Control Framework. Buildings. 2025; 15(17):3114. https://doi.org/10.3390/buildings15173114

Chicago/Turabian Style

Chen, Hongjian, Duoyu Sun, Yuyu Sun, Yong Zhang, and Huan Yang. 2025. "Multi-Agent Deep Reinforcement Learning-Based HVAC and Electrochromic Window Control Framework" Buildings 15, no. 17: 3114. https://doi.org/10.3390/buildings15173114

APA Style

Chen, H., Sun, D., Sun, Y., Zhang, Y., & Yang, H. (2025). Multi-Agent Deep Reinforcement Learning-Based HVAC and Electrochromic Window Control Framework. Buildings, 15(17), 3114. https://doi.org/10.3390/buildings15173114

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