Ensemble Machine Learning Algorithms for Thermal Comfort Prediction in HVAC Systems of Smart Buildings

Abstract

Predicting the thermal comfort of building occupants is of paramount importance in the operation of smart buildings, providing a data-driven approach to control Heating, Ventilation, and Air Conditioning (HVAC) systems for managing occupant thermal comfort and energy use, which aligns with modern sustainability and efficiency goals. Recently, ensemble machine learning (ML)-based thermal comfort prediction models have been proposed to provide more accurate estimation of thermal comfort; however, these efforts often lack a systematic and comprehensive evaluation across a wide range of ML models within a single study. To address this gap, this study presents a systematic comparative analysis of four ensemble ML frameworks (bagging, boosting, stacking, and voting) with six basic ML algorithms (Logistic Regression, K-Nearest Neighbors, Support Vector Machine, Decision Tree, Multilayer Perceptron, and Multinomial Na & iuml;ve Bayes) and six advanced ensemble ML algorithms (Random Forest, Rotation Forest, Extra Trees, Gradient Boosting Classifier, Histogram Gradient Boosting Classifier, and Extreme Gradient Boosting). The analysis is conducted using the widely recognized ASHRAE Global Thermal Comfort Database II, providing both 3-point and 7-point Thermal Sensation Vote (TSV) predictions. Accuracy, precision, recall and F1 metrics are used for evaluation and 10-fold cross validation is applied for further comparison. The results demonstrate the Histogram Gradient Boosting (HGB) algorithm achieved the highest F1 score (0.638) for 7-point TSV prediction whereas the Random Forest (RF) algorithm provided the highest F1 score (0.549) for 7-point TSV prediction. In practice, these findings suggest that integrating RF and HGB models into Building Management Systems or IoT-based HVAC platforms can support real-time adaptive control, helping practitioners to reduce energy use while maintaining occupant comfort.