暖通空调
非线性自回归外生模型
强化学习
控制器(灌溉)
计算机科学
能源消耗
电能消耗
PID控制器
空调
控制理论(社会学)
需求响应
能量(信号处理)
汽车工程
自回归模型
人工神经网络
控制工程
功率(物理)
工程类
人工智能
控制(管理)
温度控制
数学
电
电气工程
电能
机械工程
统计
生物
量子力学
农学
计量经济学
物理
作者
Suroor M. Dawood,Alireza Hatami,Raad Z. Homod
标识
DOI:10.1080/19401493.2022.2099465
摘要
This paper presents Model-based Reinforcement Learning (MB-RL) techniques to control the indoor air temperature, and CO2 concentration level, and minimize the energy consumption of the heating, ventilating, and air conditioning (HVAC) systems, simultaneously. For this purpose, a trade-off is made between maintaining indoor comfort levels and minimizing energy consumption. The control of the HVAC system is performed using the Deterministic Policy RL (DP-RL) method. Moreover, the nonlinear autoregressive exogenous neural network (NARX-NN) is employed as an approximation function with DP-RL method to provide a hybrid DP-NARX-RL controller. By applying the DP-RL and DP-NARX-RL controllers to the HVAC system of a typical building, parameters such as the indoor comfort levels, the electrical power, and energy consumed, and the energy costs at various pricing schemes are evaluated for two case studies. In both cases, the results show the better performance of DP-NARX-RL compared to DP-RL, RL, and PID controllers.
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