Data-Driven Linear Quadratic Control of Multizone Building VAV Units Using Q-Learning

This paper introduces an innovative data-driven control methodology for variable air volume (VAV) HVAC systems in non-residential buildings with fluctuating occupancy patterns. A reinforcement learning approach leveraging model-free optimal linear quadratic control principles is developed. The algorithm utilizes Bellman dynamic programming to derive a quality function that enables control decisions based entirely on system-generated data, encompassing both building dynamics and occupant behavior. The approach is validated through simulations conducted on a recently installed HVAC-VAV system in a university building. Results demonstrate the method’s effectiveness in maintaining optimal thermal comfort while minimizing airflow requirements of VAV units, directly correlating with reduced energy consumption per room. This approach proves particularly valuable in non-residential settings where occupancy levels vary significantly and unpredictably throughout the day.