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- Title
基于强化学习的室内温湿度联合控制方法研究.
- Authors
陈长成; 安晶晶; 王闯; 段晓绒
- Abstract
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 onoff 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
- Publication
Science Technology & Engineering, 2024, Vol 24, Issue 12, p5123
- ISSN
1671-1815
- Publication type
Article
- DOI
10.12404/j.issn.1671-1815.2302895