Comparative Study of Deep Q-Network-Based Reinforcement Learning and Model Predictive Control for Zone Comfort and Energy Optimization in VAV-Controlled HVAC
Jul 2026· International Conference on Computer Aided Design· pp. 1-6· 0 citations· 12 references
Abstract
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.
HVAC systems represent a major share of building energy consumption. Traditional control strategies are limited in coordinating energy-comfort tradeoffs across multiple zones simultaneously. Reinforcement learning (RL) offers adaptive, data-driven control that optimizes performance over time. However, deploying learned...
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