(untitled) (DIGITAL TRANSFORMATION AND ARTIFICIAL INTELLIGENCE, 2025)
This paper addresses the improvement of energy efficiency in Heating, Ventilation, and Air Conditioning (HVAC) systems within large commercial buildings. It discusses the development and implementation of an Artificial Intelligence AI control platform integrated into the existing Building Management System BMS. A hybrid control algorithm is proposed, combining Model Predictive Control MPC, Reinforcement Learning RL, and Trim & Respond heuristics. This approach solves a multi-criteria optimization problem: minimizing energy consumption while strictly adhering to thermal comfort and Indoor Air Quality IAQ standards. The effectiveness of the approach is demonstrated through a pilot implementation in a large shopping and entertainment center. The results indicate a 23% reduction in ventilation electricity consumption compared to the baseline scenario, while maintaining CO₂ levels and temperature within regulatory ranges. The findings are benchmarked against international studies, confirming the viability of hybrid AI strategies without the need for major equipment retrofits.