Abstract
Vacuum glass is widely used in many construction applications, including single-family homes, as a proven energy-saving method with outstanding heat preservation characteristics. The thermal insulation performance of vacuum glass is closely related to its heat transfer coefficient. In this study, we applied neural network methods to predict the heat transfer coefficients of vacuum glass. Using MATLAB, a neural network intelligence model was established, and the traditional back-propagation neural network (BPNN) was optimised. First, a genetic algorithm was used to reduce the dimensions of the independent variable. Then, the Mind Evolutionary Computation algorithm was used to optimise the initial weight and threshold. Using the optimised BPNN intelligence model to predict the heat transfer coefficient of vacuum glass insulation, we derived an average absolute error of 0.0076.
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