Prediction of heat transfer performance of vacuum glass based on extreme gradient boosting algorithm

Abstract

In this paper, a non-stationary detection method based on the artificial intelligence algorithm XGBoost is proposed for the detection of the U-value of the vacuum glass. By analyzing the heat transfer characteristics of vacuum glass and considering the detection efficiency, the features are selected as hot end temperature, ambient temperature, and characteristic temperature change rate. In this paper, the training effect of a model is measured comprehensively by the scores of MAE, MSE, and R2. Three models, KNN, GBDT, and XGBoost, are used to train the dataset and compare the prediction results. After the comparison, XGBoost has the best prediction effect. Finally, the fitted model is validated by 5*2 nested cross-loop, and the analysis results show that the fitted model has better stability, which greatly enhances the credibility of the model. After a series of experiments, it is known that the small sample of non-stationary method and multiple interference problems can all be solved by XGBoost algorithm with certain stability, which can provide ideas for further industrialized testing.