基于强化学习的室内温湿度联合控制方法研究

为解决目前风机盘管控制方法仅以室内温度作为单一控制对象且忽略湿度的问题,本文以采用风机盘管加新风系统的北京某办公建筑为研究对象,提出一种基于动作干预的强化学习控制方法对风机盘管的送风量进行调节,以期获得更佳的室内温度和相对湿度联合控制满足率。本文利用TensorFlow部署强化学习算法,在TRNSYS中建立建筑空调系统仿真模型,利用自开发的TRNSYS-Python联合仿真平台对所提算法进行训练、测试和评估。研究结果表明,与传统的通断控制和基于规则的控制方法相比,本研究提出的控制方法可以将室内温度和相对湿度联合控制满足率提高9.5%以上。可见该方法具有工程应用价值,为提高建筑室内热舒适提供了新的研究思路。

Research on 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. In this paper, 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 using a self-developed TRNSYS-Python co-simulation platform. The results show that the proposed control method in this paper 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 this method is valuable in engineering application and provides a new research idea for improving indoor thermal comfort in buildings.