What question did this study set out to answer?

The study aims to validate the environmental performance of GenAI in generating designs for optimal daylighting.

April 16, 2026Open Access

Adopting Generative Artificial Intelligence in Daylighting Design Process

Key Points

The study aims to validate the environmental performance of GenAI in generating designs for optimal daylighting.
Explored the fine-tuning of a Low-Rank Adaptation (LoRA) model for generating Daylight Autonomy heatmaps.
Controlled window-to-wall ratio (WWR) as the sole variable for design outputs.
Conducted a one-sample t-test on 120 samples to assess fitting rates to target daylighting categories.
Achieved strong similarity in Daylight Autonomy maps with SSIM values ranging from 0.694 to 0.851 for different categories.
Generated designs showed 83% fit for high, 68% for moderate, and 75% for low daylight autonomy categories, surpassing random chance.

Abstract

Generative Artificial Intelligence (GenAI) is increasingly adopted in architectural design and widely investigated in the research field. However, several limitations hinder its broader integration into design processes. Among these, the lack of validation beyond aesthetic assessment remains a research gap, especially regarding the environmental performance of Artificial Intelligence (AI)-generated designs. While current research primarily applies GenAI to exterior and urban-scale environmental assessments, this study focuses on GenAI-driven active control of interior daylighting. An experimental study explored the fine-tuning of a Low-Rank Adaptation (LoRA) model to generate Daylight Autonomy (DA) heatmaps with controlled window-to-wall ratio (WWR). Experiment scope was bounded by the control of WWR as the sole variable, while space type, geographic location, dimensions, orientation, finishing materials, and other design parameters were kept constant. The proposed workflow enabled the generation of interior designs that achieve target Spatial Daylight Autonomy (sDA) ranges, classified as “High,” “Moderate,” or “Low.” As a result, Structural Similarity Index Measure (SSIM) confirmed strong similarity between AI-generated and simulation-based DA maps, with average values of 0.851, 0.740, and 0.694 for the “High,” “Moderate,” and “Low” categories, respectively. In addition, a one-sample t-test was conducted on 120 generated samples—40 per category—verifying that generated designs fit target sDA ranges beyond random chance (33.33%). 83% of generated designs using “High” prompt keyword fit target range (t(39) = 8.086), while fitting rates reached 68% for “Moderate” (t(39) = 4.560) and 75% for “Low” category (t(39) = 6.014). Overall, the findings confirm that the proposed GenAI-driven workflow can reliably control daylighting performance, supporting the transition of GenAI from a purely aesthetic visualization tool to a validated, performance-oriented method for early-stage architectural design.

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