Optimizing Energy Efficiency, Thermal Comfort, and Indoor Air Quality in HVAC Systems Using a Robust DRL Algorithm

Abstract

This study presents a novel deep reinforcement learning (DRL) framework designed to optimize energy efficiency, thermal comfort, and indoor air quality, particularly focusing on CO2 levels, in Heating, Ventilation, and Air Conditioning (HVAC) systems. Existing DRL methods often face challenges in maintaining control effectiveness under environmental disturbances, leading to higher energy consumption and reduced system performance. To address these limitations, we propose the Decoupled Adversarial Long Short-Term Memory and Proximal Policy Optimization (DAL-PPO) algorithm, which integrates a Decoupled Adversarial Strategy (DAP) with Long Short-Term Memory networks (LSTM) to better handle temporal dynamics and disturbances. Using the EnergyPlus simulation tool and PyTorch, DAL-PPO was evaluated in a disturbed experimental setting, achieving a 15% reduction in PMV and CO2 levels and an 8% decrease in energy consumption compared to traditional reinforcement learning algorithms. These results highlight DAL-PPO’s ability to maintain system stability and efficiency even under dynamic disturbances, offering a significant improvement in HVAC system control and contributing to the development of more energy-efficient and resilient buildings.