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Volume 16
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10.3390/buildings16153127
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Daylighting and Glare Optimization in University Classrooms Based on Parametric Simulation and Machine Learning: A Case Study of Yunnan University

by
Yaoning Yang
1,
Tinggang Fu
1,
Jingyi Ye
1,*,
Renpei Zhao
1,
Jinyao Lei
1,
Wei Jiang
2,
Siqi Zeng
1,
Jialu Dai
1,
Yaqi Chen
1,
Jingbo Xia
3,
Yuyao Zhu
1 and
Yingli Zhu
3
1
School of Architecture and Urban Planning, Yunnan University, Kunming 650500, China
2
School of Materials Science and Engineering, Tongji University, Shanghai 201804, China
3
School of Art and Design, Yunnan University, Kunming 650091, China
*
Author to whom correspondence should be addressed.
Buildings 2026, 16(15), 3127; https://doi.org/10.3390/buildings16153127
Submission received: 6 July 2026 / Revised: 30 July 2026 / Accepted: 4 August 2026 / Published: 6 August 2026
(This article belongs to the Topic Novel Tools and Methods for Climate-Adaptive Urban Development)
Versions Notes

Abstract

Low-latitude plateau classrooms, such as those in Kunming, experience intense solar radiation that often causes insufficient far-window illumination, excessive near-window brightness, and viewing-direction glare under side-lighting conditions. To investigate this spatial imbalance, field surveys of nine classrooms were used to define realistic parameter ranges, while 100 parametric design cases were evaluated through annual simulation, machine learning, and SHAP analysis. The viewing-direction glare model achieved a test-set R2 of 0.819, indicating adequate predictive performance for factor interpretation. Compared with simply increasing the window-to-wall ratio (WWR), coordinated control of classroom geometry, window configuration, and surface reflectance produced a more balanced luminous environment. Daylight availability and excessive illuminance were primarily governed by WWR and window reveal depth, whereas glare was more strongly influenced by seating position, viewing direction, window width, and orientation. Classroom-wide averages may therefore conceal localized glare experienced by students. A moderate WWR of 0.26–0.40 combined with a window reveal depth of 0.75–1.17 m emerged as a preferable strategy within the investigated design space. These findings support desktop-level daylight assessment and student-perspective glare evaluation in the design and renewal of ordinary side-lit classrooms in Kunming and comparable low-latitude plateau regions.
Keywords: low-latitude plateau; university classrooms; daylighting; glare; parametric simulation; machine learning; luminous-environment optimization low-latitude plateau; university classrooms; daylighting; glare; parametric simulation; machine learning; luminous-environment optimization

Share and Cite

MDPI and ACS Style

Yang, Y.; Fu, T.; Ye, J.; Zhao, R.; Lei, J.; Jiang, W.; Zeng, S.; Dai, J.; Chen, Y.; Xia, J.; et al. Daylighting and Glare Optimization in University Classrooms Based on Parametric Simulation and Machine Learning: A Case Study of Yunnan University. Buildings 2026, 16, 3127. https://doi.org/10.3390/buildings16153127

AMA Style

Yang Y, Fu T, Ye J, Zhao R, Lei J, Jiang W, Zeng S, Dai J, Chen Y, Xia J, et al. Daylighting and Glare Optimization in University Classrooms Based on Parametric Simulation and Machine Learning: A Case Study of Yunnan University. Buildings. 2026; 16(15):3127. https://doi.org/10.3390/buildings16153127

Chicago/Turabian Style

Yang, Yaoning, Tinggang Fu, Jingyi Ye, Renpei Zhao, Jinyao Lei, Wei Jiang, Siqi Zeng, Jialu Dai, Yaqi Chen, Jingbo Xia, and et al. 2026. "Daylighting and Glare Optimization in University Classrooms Based on Parametric Simulation and Machine Learning: A Case Study of Yunnan University" Buildings 16, no. 15: 3127. https://doi.org/10.3390/buildings16153127

APA Style

Yang, Y., Fu, T., Ye, J., Zhao, R., Lei, J., Jiang, W., Zeng, S., Dai, J., Chen, Y., Xia, J., Zhu, Y., & Zhu, Y. (2026). Daylighting and Glare Optimization in University Classrooms Based on Parametric Simulation and Machine Learning: A Case Study of Yunnan University. Buildings, 16(15), 3127. https://doi.org/10.3390/buildings16153127

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