Affiliation:
1. Gridsum Inc., 229 North 4th Ring Rd., Beijing 100083, China
Abstract
Reinforcement learning (RL) is being gradually applied in the control of heating, ventilation and air-conditioning (HVAC) systems to learn the optimal control sequences for energy savings. However, due to the “trial and error” issue, the output sequences of RL may cause potential operational safety issues when RL is applied in real systems. To solve those problems, an RL algorithm with dual safety policies for energy savings in HVAC systems is proposed. In the proposed dual safety policies, the implicit safety policy is a part of the RL model, which integrates safety into the optimization target of RL, by adding penalties in reward for actions that exceed the safety constraints. In explicit safety policy, an online safety classifier is built to filter the actions outputted by RL; thus, only those actions that are classified as safe and have the highest benefits will be finally selected. In this way, the safety of controlled HVAC systems running with proposed RL algorithms can be effectively satisfied while reducing the energy consumptions. To verify the proposed algorithm, we implemented the control algorithm in a real existing commercial building. After a certain period of self-studying, the energy consumption of HVAC had been reduced by more than 15.02% compared to the proportional–integral–derivative (PID) control. Meanwhile, compared to the independent application of the RL algorithm without safety policy, the proportion of indoor temperature not meeting the demand is reduced by 25.06%.
Subject
Building and Construction,Civil and Structural Engineering,Architecture
Reference43 articles.
1. Understanding energy demand behaviors through spatio-temporal smart meter data analysis;Niu;Energy,2021
2. Experimental evaluation of model-free reinforcement learning algorithms for continuous HVAC control;Biemann;Appl. Energy,2021
3. Geng, G., and Geary, G.M. (1993, January 13–16). On performance and tuning of PID controllers in HVAC systems. Proceedings of the IEEE International Conference on Control and Applications, Vancouver, BC, Canada.
4. A review of building climate and plant controls, and a survey of industry perspectives;Royapoor;Energy Build.,2018
5. Theory and applications of HVAC control systems–A review of model predictive control (MPC);Afram;Build. Environ.,2014
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献