Predicting and extracting thermal behavior rules of hydronic thermal barrier with interpretable ensemble learning in the heating season

Abstract

Hydronic thermal barrier (HTB) creates dynamic thermal resistance in buildings, enabling buildings to adapt to outdoor weather fluctuations and regulate indoor environments. However, the lack of a comprehensive model to predict thermal behavior and explain the underlying rules is a significant limitation to its real-life situations. Additionally, the high computational cost of traditional numerical modelling also poses a challenge. To address these issues, this study proposed a framework for predicting HTB thermal behavior and extracting rules. A benchmark model was first introduced that incorporates coupled physical fields and accounts for multi-source uncertainties, reducing rule failures resulting from system changes. A data-driven prediction model was then developed, using machine learning and the finite element method, to clarify thermal behavior during the heating season in cold and severely cold regions of China. To enhance model generalization and applicability, three typical feature selection methods were employed. A comprehensive trial involving five ensemble learning models was conducted. Recursive Feature Elimination (RFE) selected the most representative features for prediction models. For heat storage efficiency, RFE combined with Light Gradient Boosting Machine achieved the best prediction performance with an R2 of 0.877, while the optimal prediction model for exergy efficiency was developed by combining RFE with Categorical Boosting with an R2 of 0.782. Finally, the profound HTB thermal behavior rules were extracted by the Shapley Additive exPlanation framework from both global and individual perspectives. Taken together, these findings can serve as a reference for the practical design and operation management of HTB.