LAUSR

The temperature and relative humidity (hereafter called humidity) in the indoor environment is closely related to the operation of its control system. The centralized control system, which has identical air inlets not only leads to uneven indoor temperatures and humidity but also highly controlled latency when interference occurs. To address this challenge and improve the precision and uniformity of temperatures and humidity in the indoor environment, we propose a distributed temperature and humidity control (DTHC) framework based on deep reinforcement learning (DRL). In this work, we use a constant temperature and humidity air-conditioning (CTHA) system for a museum as a case study to validate the optimization performance of the proposed controller. The state–action space, reward function, and DRL network structure are proposed. The air flow rate of multiple air inlets of CTHA is adjusted according to the feedback from the distributed temperature and humidity sensors. We develop a DRL-based computational fluid dynamics (CFD) experiment platform to evaluate the proposed mechanism. The experiment results show that our approach can improve the precision and uniformity of the temperature and humidity while enhancing the anti-interference capability of the control system. The adjustment time and energy consumed to reach the desired indoor air temperatures and humidity are reduced compared with rule-based methods.