Hierarchical graph-based method for static daylight prediction of 3D irregular office buildings

Efficient daylight prediction in geometrically complex office buildings remains challenging due to computational constraints and oversimplified representations in existing methods. While data-driven approaches accelerate simulations tenfold, they often neglect architectural form sensitivity and window placement effects. This study overcomes these limitations through a novel graph-based framework integrating physical daylight principles with machine learning. We develop a hierarchical semantic grammar for building topology representation and introduce dihedral angle-based encoding to capture critical glazing-to-space geometric relationships as graph edge features. An adaptive graph convolutional network subsequently aggregates multi-scale neighbor information for light transport modeling. Validated against Radiance benchmarks, the framework demonstrates an 80% accuracy improvement in daylight factor prediction while achieving 30% greater computational efficiency than comparable CNN methods. This approach effectively resolves the persistent accuracy-speed trade-off in performance simulation, enabling robust daylight optimization for sustainable design.