Abstract
This study introduces an intelligent control system for HVAC (Heating, Ventilation, and Air Conditioning) based on the Random Forest algorithm, designed to predict temperature, humidity, and energy consumption in real-time. By leveraging machine learning techniques, the proposed approach optimizes air conditioning performance to enhance energy efficiency and maintain optimal indoor environmental conditions. The experimental results demonstrate the high predictive accuracy of the Random Forest model, achieving R2values of 0.99 for temperature, 0.91 for humidity, and 0.97 for energy consumption. These results significantly outperform traditional control methods, highlighting the potential of artificial intelligence in improving both energy efficiency and system adaptability in modern HVAC systems.