Screen Façade Pattern Design Driven by Generative Adversarial Networks and Machine Learning Classification for the Evaluation of a Daylight Environment

This research seeks to identify optimised screen façade patterns and ratios for the effective management of daylight ingress and glare effects. It employs generative adversarial networks (GANs) to generate pattern variations and further evaluates the resultant variations through daylight simulations for application in screen façades. The generated pattern data were classified by hierarchical clustering to distinguish distinct feature groups, and they were subsequently utilised as façade configurations. The pattern data were assessed through daylight performance metrics: spatial daylight autonomy (sDA), annual sunlight exposure (ASE), and daylight glare probability (DGP). The results of the annual-based simulations indicate that façade patterns with frame ratios in the range of 50–65% are useful in reducing the areas exposed to intensive glare on the façade side while maintaining the minimum required lighting conditions. The overall influence of screen façades on interior daylighting in a large space (e.g., 10 m × 10 m) was found to be limited. Their performance is notable in reducing glare discomfort areas within approximately 2.5 m of south-facing façades. This study supports an application strategy in which screen façades are used to manage the extent of areas exposed to daylight ingress within an interior space.