Development of Chiller Plant Models in OpenAI Gym Environment for Evaluating Reinforcement Learning Algorithms

Xiangrui Wang, Qilin Zhang, Zhihua Chen, Jingjing Yang and Yixing Chen ()
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Xiangrui Wang: College of Civil Engineering, Hunan University, Changsha 410082, China
Qilin Zhang: College of Civil Engineering, Hunan University, Changsha 410082, China
Zhihua Chen: Department of Building Science, School of Architecture, Tsinghua University, Beijing 100084, China
Jingjing Yang: College of Civil Engineering, Hunan University, Changsha 410082, China
Yixing Chen: College of Civil Engineering, Hunan University, Changsha 410082, China

Energies, 2025, vol. 18, issue 9, 1-28

Abstract: To face the global energy crisis, the requirement of energy transition and sustainable development has emphasized the importance of controlling building energy management systems. Reinforcement learning (RL) has shown notable energy-saving potential in the optimal control of heating, ventilation, and air-conditioning (HVAC) systems. However, the coupling of the algorithms and environments limits the cross-scenario application. This paper develops chiller plant models in OpenAI Gym environments to evaluate different RL algorithms for optimizing condenser water loop control. A shopping mall in Changsha, China, was selected as the case study building. First, an energy simulation model in EnergyPlus was generated using AutoBPS. Then, the OpenAI Gym chiller plant system model was developed and validated by comparing it with the EnergyPlus simulation results. Moreover, two RL algorithms, Deep-Q-Network (DQN) and Double Deep-Q-Network (DDQN), were deployed to control the condenser water flow rate and approach temperature of cooling towers in the RL environment. Finally, the optimization performance of DQN across three climate zones was evaluated using the AutoBPS-Gym toolkit. The findings indicated that during the cooling season in a shopping mall in Changsha, the DQN control method resulted in energy savings of 14.16% for the cooling water system, whereas the DDQN method achieved savings of 14.01%. Using the average control values from DQN, the EnergyPlus simulation recorded an energy-saving rate of 10.42% compared to the baseline. Furthermore, implementing the DQN algorithm across three different climatic zones led to an average energy savings of 4.0%, highlighting the toolkit’s ability to effectively utilize RL for optimal control in various environmental contexts.

Keywords: reinforcement learning; chiller plant; OpenAI Gym; AutoBPS; optimal control (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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