HVAC control based on reinforcement learning and fuzzy reasoning: Optimizing HVAC supply air temperature, flow rate, and velocity
Leehter Yao, Li-Yu Huang, J. C. Teo
Abstract
This paper proposes a heating, ventilation and air conditioning (HVAC) control approach based on reinforcement learning (RL) and fuzzy reasoning to collectively optimize HVAC supply air temperature , flow rate, and velocity. Three possible actions are chosen including HVAC supply air temperature , flow rate and velocity. While supply air temperature and flow rate are included in the action space, air velocity is calculated using a newly formulated mathematical equation based on the selected air flow rate and certain system parameters. The Predicted Mean Vote (PMV) model is used to evaluate the thermal comfort based on HVAC supply air temperature, flow rate, and velocity, enabling the optimization of thermal comfort and electricity cost based on HVAC supply air temperature, flow rate, and velocity. To accurately represent the intricate user preferences regarding thermal comfort and electricity cost, fuzzy logic is employed to implement the reward function. Experimental results demonstrate that the proposed approach allows the RL agent to learn a superior intelligence, as evidenced by its action of increasing the HVAC supply air velocity to achieve the same PMV without decreasing the indoor temperature too much. The proposed RL framework, which optimizes HVAC supply air temperature, flow rate, and velocity together, achieves on average 6.16 % higher energy cost savings and 15.15 % better thermal comfort compared to RL methods that only optimize HVAC supply air temperature.