Rapid detection method for insulation performance of vacuum glass based on ensemble learning

For a long time, the use of steady state method to detect the thermal insulation performance of vacuum glass caused some problems such as long detection period, many influencing factors, inaccurate detection, etc. In order to improve the efficiency of vacuum glass insulation performance detection and reduce the cost of vacuum glass industrialization, the ensemble learning method for rapid detection of vacuum glass insulation performance is studied. Firstly, the correlation between variables and the distribution of variables are analyzed based on unsteady state method. The temperature-related variables and heat transfer coefficient are used as the input variables and target variables of the model. Then, three models and one model are selected as the first and second layers of stacking model based on five-fold cross-validation and Spearman correlation analysis. Finally, the heat transfer coefficient characterizing the thermal insulation performance of vacuum glass is predicted by the designed 3 + 1 stacking model. We involve 10 single models and other 11 ensemble models to verify the effectiveness of the method. The experimental results show that the 3 + 1 stacking model based on five-fold cross-validation and Spearman correlation analysis has the best prediction effect, which outperforms single model and other ensemble models. It improves the generalization ability of prediction model.