Research on Optimized Design of Rural Housing in Cold Regions Based on Parametrization and Machine Learning

With the rapid development of urbanization, the emergence of more self-built buildings in the countryside has brought about energy waste problems and decreased comfort. Achieving the low-carbon goal and improving the quality of the human living environment through architectural and planning means have become vital issues. In this study, from a parametric perspective, model building and performance simulation are carried out using Rhino and Grasshopper, and a multi-objective optimization method and a neural network model are used as the theoretical basis to train the prediction model after data collection and processing. The model validation of R 2 = 0.988 and MSE = 0.0148 indicates that the model can accurately reflect the program’s performance. By establishing a rapid prediction model for the performance of rural residential buildings, decision-makers can perform performance predictions under various parameter combinations at the early design stage, facilitating the screening of building types with high energy consumption and costs. The method can improve the efficiency of decision-making at the early stage of design, help save decision-making costs by screening high-energy-consuming building types, improve the living conditions of residents, reduce carbon emissions, and contribute to the sustainable development of residential building renewal design in rural areas.