A unified control framework of HVAC system for thermal and acoustic comforts in office building

Intelligent building system attracts more and more attention in both academic and industrial communities. Learning human comfort requirements and incorporating it into building control system is one of the important issues. In the traditional HVAC control system, the thermal comfort and the acoustic comfort are often conflicted and we lack of a scheme to trade off them well. In this paper, we propose a unified control framework based on reinforcement learning to balance the multiple dimension comforts, including the thermal and acoustic comforts. We utilize the user's complaints in thermal and acoustic sensations as feedback and combine the current environment and devices information to learn the personalized optimal control policy using online Q-learning. The challenge caused by the complaints is coped with an incorporated perception estimation scheme in the Q-learning reward design. Both simulation results and the field experimental results demonstrate the effectiveness of the algorithm, especially in the adaptivity to the individual tradeoff between thermal and acoustic comfort.