Research on façade design for cold-region buildings based on artificial neural networks and parametric modeling techniques

• Published in: Nonlinear engineering Vol. 14; no. 1; pp. 111776 - 10
• Main Author: Huan, Youxiang
• Format: Journal Article
• Language: English
• Published: Berlin De Gruyter 18.08.2025; Walter de Gruyter GmbH
• Subjects: Artificial neural networks; Building design; Cold regions; Construction costs; cost of structure; Data processing; Decision making; Deep learning; Design; Energy consumption; façade design; Green buildings; load intensity; Machine learning; Neural networks; neural networks and parametric modeling; Prediction models; Residential buildings; thermal index
• ISSN: 2192-8029; 2192-8010; 2192-8029
• DOI: 10.1515/nleng-2025-0136

This work aims to use deep learning techniques to model the thermal performance of walls in buildings located in cold regions. Upon completion of the data processing and collection steps, we theoretically train the prediction model using a neural network. In the first phases of residential building design in cold regions, decision-makers may execute performance forecasts across diverse parameter combinations. The creation of an expedited predictive model for the energy efficiency of residences in frigid areas makes this feasible. This facilitates the exclusion of building types characterized by elevated energy usage and expenses. The strategy may lower decision-making expenses and enhance decision-making efficiency during the first design phases by filtering out high-energy-consuming building kinds. This research concludes that the machine learning model enhances the building’s performance. The optimum design variable values identified in this research may serve as a reference for architects and designers aiming to meet their economic and environmental objectives in passive structures. The construction cost, thermal index, and load intensity of the building may be calculated with more accuracy by following the right procedures.