Application of Machine Learning Techniques for Predicting Heating Coil Performance in Building Heating Ventilation and Air Conditioning Systems

Heating systems in a building’s mechanical infrastructure account for a significant share of global building energy consumption, underscoring the need for improved efficiency. This study evaluates 31 predictive models—including neural networks, gradient boosting (XGBoost), bagging, and multiple linear regression (MLR) as a baseline—to estimate heating-coil performance. Experiments were conducted on a water-based air-handling unit (AHU), and the dataset was cleaned to eliminate illogical and missing values before training and validation. Among the evaluated models, neural networks, gradient boosting, and bagging demonstrated superior accuracy across various error metrics. Bagging offered the best balance between outlier robustness and pattern recognition, while neural networks showed strong capability in capturing complex relationships. An input-importance analysis further identified key variables influencing model predictions. Future work should focus on refining these modeling techniques and expanding their application to other HVAC components to improve adaptability and efficiency.