Prediction of insulation performance of vacuum glass based on cascade forest model

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.