Comparative Study of Deep Q-Network-Based Reinforcement Learning and Model Predictive Control for Zone Comfort and Energy Optimization in VAV-Controlled HVAC
HVAC systems account for a major part of building energy consumption, making advanced control strategies essential. This paper compares Deep Q-Network (DQN)-based reinforcement learning and model predictive control (MPC) for thermal comfort and energy optimization in a VAV-controlled HVAC system. Both approaches are evaluated on a realistic smart building platform under dynamic conditions, including real outdoor temperature data and varying occupancy profiles. Results show that MPC achieves smoother control actions and better energy efficiency compared to DQN, whose discretized action space introduces oscillatory behavior. However, DQN demonstrates greater robustness to uncertainty without requiring an explicit system model. These findings suggest that hybrid strategies combining both approaches could further enhance real-world HVAC control performance.