Prediction of Heat Transfer in Building Walls of Different Materials Using Neural Networks and Finite Difference Methods

This study introduces a hybrid framework that integrates transient numerical simulations with artificial neural networks (ANNs) to analyze and predict heat transfer in building walls. The framework is applied to ten different material–insulation combinations. Using the Leapfrog–Hopscotch (LH) finite difference scheme, we evaluated dynamic heat transfer and identified optimal insulation thicknesses for buildings in the cold continental climate of Bukhara. An ANN model was trained and validated on a dataset generated from 410 simulated wall configurations. The model achieved high predictive accuracy, with a mean squared error below 0.005. The thickness of the outer material layer ranged from 20 cm to 35 cm, while the inner layer thickness varied from 1 cm to 3 cm. Among the materials analyzed, glass wool + steel and gypsum + brick demonstrated superior insulation performance by minimizing heat loss most effectively, with values as low as 361,234 W/m2 and 4,983,441 W/m2, respectively, at 35 cm wall thickness. These findings underscore the potential of combining ANN-based predictions with physics-based simulations to design energy-efficient building envelopes in cold climates.