This study presents the development of a real-time AI-based adaptive HVAC control system aimed at improving energy efficiency and thermal comfort in indoor environments. HVAC systems are major contributors to building energy consumption, making their optimization essential for sustainable operations. The proposed system integrates sensors, machine learning models, and control mechanisms to dynamically adjust system parameters based on real-time environmental conditions. An experimental setup was implemented to collect data on temperature, humidity, CO₂ levels, and energy usage. The results demonstrate a significant reduction in energy consumption along with improved thermal comfort indices. Statistical analysis confirms the reliability and effectiveness of the proposed approach. The system shows strong adaptability to changing conditions, ensuring consistent performance. The findings highlight the potential of AI-driven control strategies in modern HVAC applications. This work contributes to the advancement of intelligent building systems and energy-efficient technologies.
Khatib et al. (Thu,) studied this question.