A Two-Stage Generative Optimization Framework for “Daylighting Schools”: A Case Study in the Lingnan Region of China

Within the framework of the Healthy China strategy, daylighting in primary and secondary schools is crucial for students’ health and learning efficiency. Most schools in China still face insufficient and uneven daylighting, along with limited outdoor solar exposure, underscoring the need for systematic optimization. Guided by the “Daylighting School” concept, this study proposes a campus design model that integrates indoor daylighting with outdoor activity opportunities and explores a generative optimization approach. The research reviews daylighting and thermal performance metrics, summarizes European and American “Daylighting School” experiences, and develops three classroom prototypes—Standard Side-Lit, High Side-Lit, and Skylight-Lit—together with corresponding campus layout models. A two-stage optimization experiment was conducted on a high school site in Guangzhou. Stage 1 optimized block location and functional layout using solar radiation illuminance and activity accessibility distance. Stage 2 refined classroom configurations based on four key performance indicators: sDA, sGA, UOD, and APMV-mean. Results show that optimized layouts improved activity path efficiency and daylight availability. High Side-Lit and Skylight-Lit classrooms outperformed traditional Side-Lit in illuminance, uniformity, and glare control. To improve efficiency, an ANN-based prediction model was introduced to replace conventional simulation engines, enabling rapid large-scale assessment of complex classroom clusters and providing architects with real-time decision support for daylight-oriented educational building design.