Prediction of thermal insulation performance of vacuum glass based on extreme random forest model

Xiaoyang Liu; Lei Wang; Yanggang Hu; Shan Hua

doi:10.1117/12.2640729

13 October 2022 Prediction of thermal insulation performance of vacuum glass based on extreme random forest model

Xiaoyang Liu, Lei Wang, Yanggang Hu, Shan Hua

Proceedings Volume 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022); 122872I (2022) https://doi.org/10.1117/12.2640729
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 2022, Wuhan, China

Abstract

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.

Citation Download Citation

Xiaoyang Liu, Lei Wang, Yanggang Hu, and Shan Hua "Prediction of thermal insulation performance of vacuum glass based on extreme random forest model", Proc. SPIE 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 122872I (13 October 2022); https://doi.org/10.1117/12.2640729

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