A SegFormer-based model for spatial Daylight Factor prediction with comprehensive obstruction encoding and regulatory validation

Stanislava Fedorova, Alejandro Pacheco Diéguez, Christoph Berkmiller, Alberto Floris, Libny Pacheco +1

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…