Abstract:  Large prediction errors in lighting models are caused by the nonlinear temporal variation and non-uniform spatial distribution of the natural light that enters the room through the window. In the case of limited data， how to achieve high-precision modeling of interior light environment under natural light is a huge challenge. To solve these problems， an interior illuminance prediction technique using principal component analysis and Bayesian optimized gradient boosting regression tree is proposed. Firstly， the algorithm performs feature reshaping by principal component analysis， fully takes into account the intrinsic correlation between various illuminance data features and preprocesses the sample data using Dummy variable. Then the random forest is used to determine the initial parameters of GBRT to improve its convergence speed and stability. Finally， cross-validation and Bayesian optimization algorithm are integrated to determine the hyperparameter combination of GBRT， so as to further improve the prediction ability of the model for indoor illumination distribution. The experimental results show that under different weather and time conditions， the R2， MAE and RMSE of 600 test samples with illuminance are 0.9912， 18 lx and 40 lx， respectively， which are superior to other algorithms and can significantly reduce sample deviation value.

Key words: daylighting prediction model, gradient boosting regression tree（GBRT）, Bayesian optimization