Joint Control Method of Indoor Temperature and Relative Humidity Based on Reinforcement Learning
In order to solve the problem that the current fan coil units control method only takes indoor temperature as a single control object and ignores humidity,an office building in Beijing with fan coil units and fresh air system was studied.To obtain a better joint control satisfaction rate of indoor temperature and relative humidity,a reinforcement learning control method based on action intervention was proposed for regulating the air supply volume of fan coil units.A reinforcement learning algorithm was deployed using TensorFlow,a building energy system simulation model was built in TRNSYS,and the proposed algorithm was trained,tested and evaluated by using a self-developed TRNSYS-Python co-simulation platform.The results show that the proposed control method can improve the joint control satisfaction rate of indoor temperature and relative humidity by at least 9.5％compared with the traditional on-off control and rule-based control.It is concluded that the proposed method is valuable in engineering application and provides a new research idea for improving indoor thermal comfort in buildings.
fan coil unitsreinforcement learningco-simulationindoor temperature and relative humidity control
万方数据
维普
