Prediction of heat transfer performance of vacuum glass based on recurrent neural network

Vacuum glass, a thermal insulation material that is highly effective, has a diverse array of applications in the manufacturing of home appliances and the improvement of energy efficiency in buildings. Accurately determining the heat transfer coefficient of vacuum glass is essential for optimising its design and application. The heat transfer coefficient of vacuum glass cannot be rapidly and batch-detected using conventional methods. Deep learning models are capable of acquiring the law of heat transfer coefficient from historical data and providing a rapid and precise prediction of the heat transfer coefficient for new samples. In recent years, recurrent neural networks (RNN) and their variants have demonstrated exceptional performance in time-series prediction tasks, thereby offering novel solutions to the issue of swiftly determining the heat transfer coefficient of the vacuum glass. The objective of this investigation is to compare three recurrent neural network models—simple RNN, long short-term memory network (LSTM), and gated recurrent unit (GRU)—in order to ascertain the most appropriate model for predicting the heat transmission coefficients of vacuum glass. The experimental results show that the prediction accuracy of the GRU model on the test set is significantly better than that of the simple RNN and LSTM models, in addition, the GRU model maintains a low computational complexity. In the future, there will be additional research conducted on deep learning models that are similar to GRU in order to enhance the interpretability and reliability of the prediction and apply them to real-time monitoring systems.