Research on vacuum glass insulation performance prediction based on unsteady state multivariate data screening and multi-model fusion self-optimization

Abstract

A self-optimization regression method based on multivariate data screening and multi-model fusion is proposed for the regression prediction of vacuum glass insulation performance. Firstly, we analyzed the positive correlation between the temperature change rate and heat transfer coefficient. Secondly, we combined the technique of multi-distribution overall diffusion to generate virtual sample data with multiple variables corresponding to the small neighborhood of the target variable, addressing the issue of insufficient sample size. We conducted quantity and threshold screening on the generated data to enhance the effectiveness of the virtual sample data. The screened data and original training set data are integrated to form new training samples. Then, the limited-memory Broyden Fletcher Goldfarb Shanno with boundary constraints (L-BFGS-B) algorithm is introduced to optimize the parameters of multiple models to improve the accuracy of model regression prediction. Finally, through the self-searching optimization framework structure, the virtual sample data screening and optimization of various model parameters are updated. Eventually, the optimal model for predicting the heat transfer coefficient of vacuum glass insulation performance is determined from multiple models. In order to validate the effectiveness of self-optimization regression method, we conducted insulation performance heat transfer coefficient predictions under 30 different ways for vacuum glass. Experimental results demonstrate that the self-optimization regression method based on multivariate data screening and multi-model fusion can obtain the most effective regression prediction model, achieving accurate, rapid and intelligent prediction of vacuum glass insulation performance.