From Simulation to Operation: AI-Based Environmental Control Systems Bridging the Performance Gap in Sustainable University Buildings – Case Study of Damietta

As The energy performance gap (EPG), defined as the discrepancy between predicted and actual building energy consumption, remains a persistent challenge in sustainable building design. This study investigates the implementation of AI-based environmental control systems to bridge the performance gap in university buildings located in Damietta, Egypt. A hybrid framework integrating calibrated Energy Plus simulation, deep learning-based load forecasting (CNN–LSTM), and reinforcement learning HVAC optimization was developed and validated using 12 months of operational data. Results indicate that AI-driven predictive control reduced the performance gap by 22–28%, improved indoor environmental quality compliance from 69% to 93%, and reduced operational costs by approximately 19%. The findings demonstrate that transitioning from static simulation-based design to adaptive AI-driven operational control significantly enhances energy reliability and sustainability outcomes in hot-humid climates.