设定值
试验台
计算机科学
暖通空调
Python(编程语言)
恒温器
能源消耗
可变风量
温度控制
模拟
重置(财务)
热舒适性
强化学习
控制理论(社会学)
汽车工程
控制工程
控制(管理)
工程类
气流
空调
人工智能
机械工程
计算机网络
物理
电气工程
金融经济学
经济
热力学
操作系统
作者
Xi Fang,Guangcai Gong,Guannan Li,Liang Chun,Pei Peng,Wenqiang Li,Xing Shi,Xiang Chen
标识
DOI:10.1016/j.applthermaleng.2022.118552
摘要
Determining a proper trade-off between energy consumption and indoor thermal comfort is important for HVAC system control. Deep Q-learning (DQN) based multi-objective optimal control strategy is designed for temperature setpoint real-time reset to balance the energy consumption and indoor air temperature. In addition, this study develops an EnergyPlus-Python co-simulation testbed to evaluate DQN control strategy in a simulation environment. A case study experiment is conducted to evaluate the performance of DQN control strategy for real-time reset of supply air temperature and chilled supply water temperature setpoint in a multi-zone building VAV system. The developed EnergyPlus-Python co-simulation testbed is used to train and test the DQN control strategy for performance analysis. The applied DQN strategy leans to update control actions (i.e. temperature setpoint) through interaction with the simulation environment. Simulation results show that the DQN control strategy is effective in finding a proper trade-off between the energy consumption of HVAC system and indoor air temperature. Meanwhile, the DQN control strategy can find a proper temperature setpoint reset sequence in smaller training episodes, and the control actions can be stable after ten DQN training episodes. This study provides a preliminary direction of deep reinforcement learning control strategy for temperature setpoint real-time reset in multi-zone building HVAC systems.
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