Explainable Machine Learning for Hospital Heating Plants: Feature-Driven Modeling and Analysis

Abstract

Hospitals are among the most energy-intensive buildings, yet their heating systems often operate below optimal efficiency due to outdated controls and limited sensing. Existing facilities often provide only a few accessible measurement points, many of which are locked within proprietary master controllers and not integrated into the Building Automation System (BAS). To address these limitations, this study proposes a data-driven feature selection approach that supports the development of gray-box emulators for complex, real-world central heating plants. A year of operational and weather data from a large hospital was used to train multiple machine learning models to predict the heating demand and return water temperature of a hospital heating plant system. The model’s performance was evaluated under reduced-sensor conditions by intentionally removing unpredictable values such as the VFD speed and flow rate. XGBoost achieved the highest accuracy with full sensor data and maintained a strong performance when critical sensors were omitted. An explainability analysis using Shapley Additive Explanations (SHAP) is applied to interpret the models, revealing that outdoor temperature and time of day (as an occupancy proxy) are the dominant predictors of boiler load. The results demonstrate that, even under sparse instrumentation, an AI-driven digital twin of the heating plant can reliably capture system dynamics.