暖通空调
热舒适性
能源消耗
强化学习
计算机科学
空调
高效能源利用
前馈
模拟
汽车工程
建筑工程
控制工程
工程类
人工智能
机械工程
物理
电气工程
热力学
作者
Guanyu Gao,Jie Li,Yonggang Wen
出处
期刊:IEEE Internet of Things Journal
[Institute of Electrical and Electronics Engineers]
日期:2020-09-01
卷期号:7 (9): 8472-8484
被引量:124
标识
DOI:10.1109/jiot.2020.2992117
摘要
Heating, ventilation, and air conditioning (HVAC) are extremely energy consuming, accounting for 40% of total building energy consumption. It is crucial to design some energy-efficient building thermal comfort control strategy which can reduce the energy consumption of the HVAC while maintaining the comfort of the occupants. However, implementing such a strategy is challenging, because the changes of the thermal states in a building environment are influenced by various factors. The relationships among these influencing factors are hard to model and are always different in different building environments. To address this challenge, we propose a deep-reinforcement-learning-based framework, DeepComfort, for thermal comfort control in buildings. We formulate the thermal comfort control as a cost-minimization problem by jointly considering the energy consumption of the HVAC and the occupants' thermal comfort. We first design a deep feedforward neural network (FNN)-based approach for predicting the occupants' thermal comfort and then propose a deep deterministic policy gradients (DDPGs)-based approach for learning the optimal thermal comfort control policy. We implement a building thermal comfort control simulation environment and evaluate the performance under various settings. The experimental results show that our approaches can improve the performance of thermal comfort prediction by 14.5% and reduce the energy consumption of HVAC by 4.31% while improving the occupants' thermal comfort by 13.6%.
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