Prediction of heat transfer of vacuum glass based on intelligent algorithm modeling

Hu, Yanggang; Wang, Lei

doi:10.1109/eiecs53707.2021.9588143

Cited by 2 publications

(2 citation statements)

References 4 publications

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“…For small sample prediction, the random forest algorithm is used to detect the thermal conductivity of vacuum glass, and the model evaluation parameters MAE, MSE, R2 have reached a good range [7]. This paper proposes to use the extreme random forest algorithm to detect the heat transfer coefficient through the continuous research of this scheme.…”

Section: Introductionmentioning

confidence: 99%

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

Liu

Wang²,

Hu³

et al. 2022

International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022)

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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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Section: Introductionmentioning

confidence: 99%

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

Liu

Wang²,

Hu³

et al. 2022

International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022)

View full text Add to dashboard Cite

show abstract

“…Heat transfer coefficient can represent the heat transfer capacity of vacuum glass and can also be denoted as U value. [4] We use a non-stationary-based detection method. The regression prediction based on small sample data is poor using the usual algorithms based on large amount of data, and often faces overfitting problems, For example, the heat transfer process modeling of vacuum glass based on LSSVM.…”

mentioning

confidence: 99%

Prediction of heat transfer performance of vacuum glass based on extreme gradient boosting algorithm

Jia

Wang

2023

Eighth International Conference on Electronic Technology and Information Science (ICETIS 2023)

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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.

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Prediction of heat transfer of vacuum glass based on intelligent algorithm modeling

Cited by 2 publications

References 4 publications

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

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

Prediction of heat transfer performance of vacuum glass based on extreme gradient boosting algorithm

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