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

Atef Gharbi (), Mohamed Ayari, Nasser Albalawi, Yamen El Touati and Zeineb Klai
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Atef Gharbi: Department of Information Systems, Faculty of Computing and Information Technology, Northern Border University, Rafha 91911, Saudi Arabia
Mohamed Ayari: Department of Information Technology, Faculty of Computing and Information Technology, Northern Border University, Rafha 91911, Saudi Arabia
Nasser Albalawi: Department of Computer Sciences, Faculty of Computing and Information Technology, Northern Border University, Rafha 91911, Saudi Arabia
Yamen El Touati: Department of Computer Sciences, Faculty of Computing and Information Technology, Northern Border University, Rafha 91911, Saudi Arabia
Zeineb Klai: Department of Computer Sciences, Faculty of Computing and Information Technology, Northern Border University, Rafha 91911, Saudi Arabia

Mathematics, 2025, vol. 13, issue 14, 1-16

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.

Keywords: reinforcement learning; proportional-integral derivative; HVAC system; energy efficiency; thermal comfort; cognitive control (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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