Study on the application of reinforcement learning in the operation optimization of HVAC system  

作　　者：Xiaolei Yuan Yiqun Pan Jianrong Yang Weitong Wang Zhizhong Huang 

机构地区：[1]School of Mechanical Engineering,Tongji University,4800 Cao’an Road,Shanghai,201804,China [2]Shanghai Research Institute of Building Sciences,Shanghai,China [3]Kuaishou Co.Ltd.,Beijing,China [4]Sino-German College of Applied Sciences,Tongji University,Shanghai,201804,China

出　　处：《Building Simulation》2021年第1期75-87,共13页建筑模拟（英文）

基　　金：This study is supported by the Thirteenth Five-Year National Key Research and Development Program“Study on the Technical Standard System for Post-evaluation of Green Building Performance”,Ministry of Science and Technology of China(No.2016YFC0700105).

摘　　要：Supervisory control can be used to optimize the HVAC system operation and achieve building energy conservation,while reinforcement learning(RL)is considered as a promising model-free supervisory control method.In this paper,we apply RL algorithm to the operation optimization of air-conditioning(AC)system and propose an innovative RL-based model-free control strategy combining rule-based and RL-based control algorithm as well as complete application process.We use a variable air volume(VAV)air-conditioning system for a single-storey office building as a case study to validate the optimization performance of the RL-based controller.We select control strategies with the rule-based control controller(RBC)and proportional-integral-derivative(PID)controller respectively as the reference cases.The results show that,for the air supply of single zone,the RL controller performs the best in terms of both non-comfortable time and energy costs of AC system after one-year exploration learning.The total energy consumption of AC system reduced by 7.7%and 4.7%,respectively compared with RBC and PID strategies.For the air supply of multi-zone,the performance of RL controller begins to outperform the reference strategies after two-year exploration learning and two-year buffer stage.From the seventh year on,RL controller performs much better in terms of both non-comfortable time and operating costs of AC system,while the operating cost of AC system is reduced by 2.7%to 4.6%compared with the reference strategies.In addition,RL controller is more suitable for small-scale operation optimization problems.

关 键 词：reinforcement learning HVAC system operation optimization control strategy VAV system energy saving 

分 类 号：TU83[建筑科学—供热、供燃气、通风及空调工程]