Intelligent HVAC Control Systems Based on Machine Learning and Deep Learning Models

Buildings are responsible for nearly 35% of global energy consumption, with heating and cooling accounting for a significant share. As urbanization and population growth accelerate, energy demand in cities continues to rise, leading to increased CO2 emissions, higher electricity costs, and greater stress on power grids. To address these challenges, real-time control systems and building automation have shown strong potential in improving energy efficiency in the built environment. In particular, optimized Heating, Ventilation, and Air Conditioning (HVAC) control systems can significantly reduce energy consumption, lower operational costs, and mitigate environmental impacts. This study investigates the application of machine learning models—including Linear Regression, Random Forest, Support Vector Machine (SVM), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU)—to predict HVAC performance. The models are evaluated using Mean Squared Error (MSE) and R2 score, with the goal of identifying the most effective approach for enabling data-driven energy optimization in buildings.