This study articulates a pioneering, integrated methodology for the optimization of fenestration and spatial configuration in residential living rooms, tailored specifically to the exigencies of Isfahan’s hot-arid climate. The overarching objective is the maximization of daylighting performance through the simultaneous evaluation of three pivotal climate-based metrics: Spatial Daylight Autonomy (sDA), Annual Sunlight Exposure (ASE), and Useful Daylight Illuminance (UDI). Distinctively, this research synergizes a Deep Learning (DL) predictive model with a genetic algorithm-based multi-objective optimization framework (Galapagos), transcending the limitations of traditional static modeling techniques. The deployed feedforward neural network exhibited exemplary predictive fidelity, yielding R² coefficients of 0.97 for UDI and ASE, and a perfect 1.00 for sDA. Subsequent interpretability analyses underscored the critical impact of room depth and Window-to-Wall Ratio (WWR) on luminous performance. The optimization protocol culminated in a definitive design archetype for a south-facing volume (4m width × 5m depth), oriented at a -1 degree azimuth. This configuration, featuring a 30% WWR distributed across two vertical apertures with a 0.90m sill height and devoid of external shading, achieved an optimal equilibrium: 100% sDA, 43% ASE, and 72% UDI. Consequently, this work establishes a robust, data-driven framework for sustainable architectural practice, offering precise parametric guidelines for daylighting efficacy in challenging climatic zones.
Najafi, A. , Rahravi Poodeh, S. and Tadayon, B. (2025). Deep Learning-Driven Optimization of Fenestration for Daylighting in Hot-Arid Climates: A Hybrid Evolutionary Framework. Journal of Design Thinking, 6(2), 281-295. doi: 10.22059/jdt.2026.408857.1176
MLA
Najafi, A. , , Rahravi Poodeh, S. , and Tadayon, B. . "Deep Learning-Driven Optimization of Fenestration for Daylighting in Hot-Arid Climates: A Hybrid Evolutionary Framework", Journal of Design Thinking, 6, 2, 2025, 281-295. doi: 10.22059/jdt.2026.408857.1176
HARVARD
Najafi, A., Rahravi Poodeh, S., Tadayon, B. (2025). 'Deep Learning-Driven Optimization of Fenestration for Daylighting in Hot-Arid Climates: A Hybrid Evolutionary Framework', Journal of Design Thinking, 6(2), pp. 281-295. doi: 10.22059/jdt.2026.408857.1176
CHICAGO
A. Najafi , S. Rahravi Poodeh and B. Tadayon, "Deep Learning-Driven Optimization of Fenestration for Daylighting in Hot-Arid Climates: A Hybrid Evolutionary Framework," Journal of Design Thinking, 6 2 (2025): 281-295, doi: 10.22059/jdt.2026.408857.1176
VANCOUVER
Najafi, A., Rahravi Poodeh, S., Tadayon, B. Deep Learning-Driven Optimization of Fenestration for Daylighting in Hot-Arid Climates: A Hybrid Evolutionary Framework. Journal of Design Thinking, 2025; 6(2): 281-295. doi: 10.22059/jdt.2026.408857.1176
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