AI-Driven Anomaly Detection for AHU Supply Airflow

We taught an AI model to understand the 'healthy' behavior of an Air Handling Unit (AHU) by predicting its normal airflow. The project was a success, developing an XGBoost model with over 96% accuracy, establishing a strong foundation for predictive maintenance by enabling earlier and more reliable fault detection.