In this paper, a new method is proposed for the intelligent prediction of the thermal insulation performance of vacuum glass, i.e., the use of cascade forest algorithm to detect the heat transfer coefficient (U-value) of vacuum glass. By constructing different intelligent algorithm models, random forest, extreme random forest and cascade forest algorithms are used. By evaluating the proposed method using mean absolute error (MAE), mean square error (MSE) and R-squared value, the cascade forest was evaluated with values of 0.0401, 0.0035 and 0.9896, respectively, and the predicted value curve was very close to the true value curve, so it was concluded that the cascade forest algorithm was superior to the random forest and extreme random forest algorithms in predicting the heat transfer coefficient of vacuum glass. In order to avoid the risk of overfitting, k-fold cross-validation was also added to each random forest in the cascade forest during the training process, and the accuracy of the cross-validated data was improved by 1% as shown by the data. It is known from the experimental results that the algorithm with cascade forest gives a new idea for the work of fast detection of heat transfer characteristics of vacuum glass based on small samples.
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.
KEYWORDS: Glasses, Data modeling, Temperature metrology, Thermal modeling, Performance modeling, Statistical modeling, Resistance, Computing systems, Testing and analysis, Radiative energy transfer
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.
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