This study introduces a comprehensive computational framework integrating image-based simulations, spatial frequency analysis, and multi-objective optimization to evaluate and optimize passive solar shading devices from an occupant-centric perspective. While traditional façade optimization primarily addresses daylight performance and glare control, critical gaps remain in objectively and simultaneously quantifying visual comfort and preference, as well as external view content and quality—both essential to user satisfaction and psychological well-being. To bridge these gaps, spatial frequency metrics, historically utilized in image classification and visual assessments, are proposed as quantitative indicators for evaluating shading devices. The methodology employs first-person interior views analyzed through advanced computational techniques—daylight glare probability, image segmentation and power spectrum analysis—to objectively assess visual comfort, view content and spatial frequency composition. The proposed framework employs an adaptive optimization algorithm that iteratively generates and refines shading device configurations, effectively balancing glare reduction, external visibility, and visual complexity. Two experimental studies validate the approach: the first systematically evaluates multiple predefined shading patterns to identify optimal characteristics, while the second demonstrates that algorithmic optimization of highly irregular shading configurations can simultaneously improve multiple visual comfort metrics, significantly outperforming regular shading patterns in terms of glare reduction, view preservation, and spatial frequency performance.
Ivanov et al. (Mon,) studied this question.