The application of machine learning (ML) modelling in daylight prediction has been a promising approach for reliable and effective visual comfort assessment. Although many advancements have been made, no standardized ML modelling framework exists in daylight assessment. In this study, 625 different building layouts were generated to model useful daylight illuminance (UDI). Two state-of-the-art ML algorithms, eXtreme Gradient Boosting (XGBoost) and random forest (RF), were employed to analyze UDI in four categories: UDI-f (fell short), UDI-s (supplementary), UDI-a (autonomous), and UDI-e (exceeded). A feature (internal finish) was introduced to the framework to better reflect real-world representation. The results show that XGBoost models predict UDI with a maximum accuracy of R² = 0.992. Compared to RF, the XGBoost ML models can significantly reduce prediction errors. Future research directions have been specified to advance the proposed framework by introducing new features and exploring new ML architectures to standardize ML applications in daylight prediction.

机器学习 (ML) 建模在日光预测中的应用一直是可靠、有效的视觉舒适度评估的一种有前途的方法。尽管已经取得了许多进步，但日光评估中尚不存在标准化的机器学习建模框架。在这项研究中，生成了 625 种不同的建筑布局来模拟有用的日光照度 (UDI)。采用两种最先进的 ML 算法，即极限梯度提升 (XGBoost) 和随机森林 (RF)，来分析四个类别的 UDI：UDI- f（未达标）、UDI- s（补充）、UDI- a（自主）和UDI- e（超过）。框架中引入了一个功能（内部饰面），以更好地反映现实世界的表现。结果表明，XGBoost 模型预测 UDI 的最大准确度为R ² = 0.992。与 RF 相比，XGBoost ML 模型可以显着减少预测误差。未来的研究方向已经确定，通过引入新功能和探索新的 ML 架构来推进所提出的框架，以标准化日光预测中的 ML 应用。