Ensemble learning framework for radiative cooling coatings in China’s buildings

Abstract

Radiative cooling (RC) coatings have emerged as a promising strategy to mitigate the urban heat island effect and improve energy performance in residential buildings. However, their effect varies significantly across different climate zones and urban configurations, underscoring the need for targeted deployment strategies. In this study, an ensemble learning framework was developed by integrating the urban canopy model with the building energy model to predict the energy performance of RC coatings on residential buildings throughout China. A dataset of 5080 cases was generated, and CatBoost demonstrated excellent predictive accuracy (R² = 0.948–0.989). SHapley Additive exPlanations analysis identified longwave radiation and building geometry as the most influential factors affecting RC coating energy performance. The trained prediction model was further applied to evaluate six representative cities across diverse climate zones, for community-level evaluation. Additionally, national-scale predictions were conducted by the framework, using simulations of 111 cities, showing RC coatings are most effective in climate zones with hot summer and warm winter, with maximum annual electricity savings of approximately 50 MWh and maximum carbon emission reductions of around 20 kg·m^-2 per year in a hypothetical residential neighborhood. In contrast, their benefits are more limited in cold climate zones due to increased heating demand. These findings provide an effective framework for optimizing RC coating deployment strategies under varying climatic conditions. Furthermore, the framework holds the potential to expand these analyses globally, enabling the evaluation of RC coatings across diverse building types and regions to support worldwide energy and carbon reduction goals.