Classrooms in severe cold regions face the dual challenge of ensuring high-quality daylighting while minimizing heating energy consumption. To address this challenge, this study develops a data-driven workflow that integrates building performance simulation, multi-objective optimization and a classification-based surrogate model, aiming to explore integrated improvements in daylighting and heating energy consumption in university classrooms. The results show that: (1) multi-objective optimization significantly enhances overall performance. Daylighting performance improves, with Spatial Daylight Autonomy (sDA) and Useful Daylight Illuminance (UDI) increasing by 0.15 and 10.67%, respectively, and Daylight Glare Probability (DGP) decreasing by 16.35%. Meanwhile, Heating Energy Consumption (Eh) is reduced by 6.20 kWh/m2; (2) SHAP analysis further identifies classroom depth, height, and glazing option as key design parameters influencing integrated daylight–thermal performance; (3) the MLP classification model achieves stable predictive accuracy, with accuracy, recall, and F1-score exceeding 0.95, demonstrating strong generalization ability. This study provides quantitative insights into the relationship between spatial parameters and daylight–thermal performance, offering researchers a method for rapidly evaluating design schemes at the early design stage.
Yan et al. (2026) studied this question.