Machine Learning for Daylight Performance Prediction

Machine learning methods are increasingly applied in daylight performance assessment due to their ability to model complex nonlinear relationships within large datasets while offering substantially faster predictions than conventional simulation workflows. Within this framework, deep learning architectures provide enhanced representational capability for capturing spatial and geometric dependencies. However, existing approaches often lack seamless integration with parametric design environments and offer limited interpretability regarding the influence of design parameters. This paper presents DayANN (Daylight Artificial Neural Network), a feedforward deep neural network developed within a structured Grasshopper-to-machine learning workflow for analyzing daylight performance in a parametrically defined office space. The method employs Climate Studio for Grasshopper to generate 288 simulation scenarios, forming the training dataset for the predictive model. The proposed framework enables automated data transfer, model training, and performance feedback within an iterative design–evaluation loop. In addition to predictive accuracy, SHAP-based interpretability is incorporated to quantify the contribution of individual daylighting parameters. The model achieved high accuracy, with R2 values of 0.988 for Useful Daylight Illuminance (UDI) and 0.947 for Daylight Factor (DF), demonstrating that DayANN serves as a computationally efficient, transparent surrogate model suitable for early-stage architectural decision-making.