A SegFormer-based model for spatial Daylight Factor prediction with comprehensive obstruction encoding and regulatory validation
Daylight compliance assessment in residential buildings requires time-consuming raytracing simulations and specialist model preparation, limiting its integration into iterative architectural design. We present a machine learning-based methodology for predicting the spatial distribution of the Daylight Factor (DF) directly within Building Information Modelling (BIM) environments, delivering results in seconds without model export or manual curation. The approach addresses five gaps not jointly covered by any existing model: external obstruction encoding including balconies, own building height, variable material reflectances, non-orthogonal room geometries, and regulatory validation. Two models are developed: a default-materials model with fixed reflectances for preliminary design checks, and a custom-materials model with variable project-specific reflectances. Both are trained on about 20,000 synthetic Radiance-simulated rooms and validated on a held-out synthetic dataset and 102 rooms from 18 real building models. The default-materials model achieves $δ_1$ = 83.4% and MAE = 0.11% DF on synthetic data, and on real buildings predicts BBR (Swedish) compliance area within 4.85 pp MAE (bias +2.31 pp) and TEK17 (Norwegian) mean room DF within 0.304% DF MAE (bias +0.287% DF). The custom-materials variant shows 1.7-2.4$\times$ higher error across all metrics. Both models concentrate accuracy in the BBR compliance-critical zone (DF < 1%), achieving a sub-1% DF MAE of 0.105% DF and Compliance IoU of 0.883 on real buildings. All cases where compliance is overestimated are borderline, within 10.7 pp of the BBR threshold (median 3.2 pp) and 0.36% DF of the TEK17 threshold (median 0.10% DF). The default-materials model is recommended as a replacement for preliminary daylight simulations; the custom-materials variant requires further development before use in final compliance verification.