Intelligent Analysis of Energy Consumption of Porous Insulating Building Materials Based on Radial Basis Function Neural Network
Year: 2025
Type: article
Abstract: To enable intelligent analysis of energy consumption in porous insulating building materials, this study proposes an energy consumption analysis model based on the Radial Basis Function (RBF) neural network. The model examines the characteristic data of porous insulating materials, identifying, and quantifying key factors that impact energy consumption. Leveraging the strong nonlinear fitting capabilities of the RBF neural network, it performs deep learning and prediction on the energy consumption data of these materials, achieving accurate and efficient forecasts. Experimental results demonstrate that the model effectively captures complex relationships within energy consumption data, achieving high prediction accuracy and stability. This approach shows promising applications in building material energy analysis, providing data support and references for energy-saving initiatives in construction.
Authors: Haozhe Pan, Xingpei Yan
Institution: Jiangsu University Jingjiang College
Language: English
Subfield: Building and Construction
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4409803722