Intelligent HVAC Control: Comparative Simulation of Reinforcement Learning and PID Strategies for Energy Efficiency and Comfort Optimization

Abstract

This study presents a new comparative analysis of the cognitive control methods of HVAC systems that assess reinforcement learning (RL) and traditional proportional-integral-derivative (PID) control. Through extensive simulations in various building environments, we have shown that while the PID controller provides stability under predictable conditions, the RL-based control can improve energy efficiency and thermal comfort in dynamic environments by constantly adapting to environmental changes. Our framework integrates real-time sensor data with a scalable RL architecture, allowing autonomous optimization without the need for a precise system model. Key findings show that RL largely outperforms PID during disturbances such as occupancy increases and weather fluctuations, and that the preferably optimal solution balances energy savings and comfort. The study provides practical insight into the implementation of adaptive HVAC control and outlines the potential of RL to transform building energy management despite its higher computational requirements.