perforated shading screens, multi-objective optimization, MCDM, shading systems, hot climates, daylight performance, Building construction, TH1-9745

Direct sunlight causes glare and reduces indoor daylight quality, making shading systems essential. This study proposes and validates a perforated shading screen (PSS) to enhance daylighting and energy efficiency. A hybrid approach integrating parametric modeling, machine learning, multi-criteria decision-making (MCDM), and genetic algorithm (GA) is used to optimize the design incorporating architects’ preferences. The Analytic Network Process (ANP) is used to assign weights to performance metrics while accounting for interdependencies. The study evaluates PSS performance in three hot climate regions—Cairo, Riyadh, and Kuching—on both south and west elevations, comparing it to traditional fins. Results show that PSS consistently outperforms fins, significantly improving daylight and energy performance. The Useful Daylight Illuminance (UDI) increased by up to 105.32%, Continuous Daylight Autonomy (CDA) by up to 11.87%, while Annual Solar Exposure (ASE), Solar Gain (SG), and Energy Use Intensity (EUI) were reduced by up to 100%, 88.07%, and 45.2%, respectively. To validate the findings, the optimal PSS design from a selected case study was 3D-printed and experimentally tested. Results confirmed enhanced daylight distribution and reduced glare, improving occupant comfort. The proposed PSS offers an effective shading solution adaptable to various climates, balancing daylighting needs and energy efficiency.