For the prediction of the U value of the heat transfer coefficient of vacuum glass, a new method is proposed in this paper. By constructing a prediction model of vacuum glass heat transfer coefficient based on extreme random forest and random forest algorithm, the prediction of U value of heat transfer coefficient is realized. This paper measures the excellence of the prediction model by using the MAE, MSE and 𝑅ଶ squared value parameters and plotting the observed curve between the predicted value and the actual value. Finally, the evaluation values of extreme forest are 0.0458, 0.0050, 0.9784, and the prediction curves are very consistent, which proves that the extreme forest prediction model has a good U value prediction ability. At the same time, by introducing the RMSE image curve, it is observed that compared with random forest, extreme forest has better generalization ability under smaller data. Aiming at the difficulty of collecting vacuum glass data sets, this paper introduces the feature importance analysis method, and the correlation between the temperature change rate and the heat transfer coefficient U is as high as 0.9882. It provides a new idea for further reducing the size of the dataset.
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.
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