A practical deep reinforcement learning framework for multivariate occupant-centric control in buildings

Abstract

Reinforcement learning (RL) has been shown to have the potential for optimal control of heating, ventilation, and air conditioning (HVAC) systems. Although research on RL-based building control has received extensive attention in recent years, there is limited real-world implementation to evaluate its performance while keeping occupants in the loop. Additionally, many HVAC systems consist of multiple subsystems, but conventional RL algorithms face significant challenges when dealing with high-dimensional action spaces. This study proposes a practical deep reinforcement learning (DRL) based multivariate occupant-centric control framework that considers personalized thermal comfort and occupant presence. Specifically, Branching Dueling Q-network (BDQ) is leveraged as the learning agent to efficiently solve the multi-dimensional control task, and a tabular-based personal comfort modeling method is applied that is naturally integrated into human-in-the-loop operations. The BDQ agent is pre-trained in a virtual environment, followed by online deployment in a real office space for 5-dimensional action control. Based on the actual deployment and real-time comfort votes, our results showed a 14% reduction in cooling energy and an 11% improvement in total thermal acceptability.