Evaluation and Optimization of Traditional Mountain Village Spatial Environment Performance Using Genetic and XGBoost Algorithms in the Early Design Stage—A Case Study in the Cold Regions of China

As urbanization advances, rural construction and resource development in China encounter significant challenges, leading to the widespread adoption of standardized planning and design methods to manage increasing population pressure. These uniform approaches often prioritize economic benefits over climate adaptability and energy efficiency. This paper addresses this issue by focusing on traditional mountain villages in northern regions, particularly examining the wind and thermal environments of courtyards and street networks. This study integrates energy consumption and comfort performance analysis early in the planning and design process, utilizing Genetic and XGBoost algorithms to enhance efficiency. This study began by selecting a benchmark model based on simulations of courtyard PET (Physiological Equivalent Temperature) and MRT (mean radiant temperature). It then employed the Wallacei_X plugin, which uses the NSGA-II algorithm for multi-objective genetic optimization (MOGO) to optimize five energy consumption and comfort objectives. The resulting solutions were trained in the Scikit-learn machine learning platform. After comparing machine learning models like RandomForest and XGBoost, the highest-performing XGBoost model was selected for further training. Validation shows that the XGBoost model achieves an average accuracy of over 80% in predicting courtyard performance. In the project’s validation phase, the overall street network framework of the block was first adjusted based on street performance prediction models and related design strategies. The optimized model prototype was then integrated into the planning scheme according to functional requirements. After repeated validation and adjustments, the performance prediction of the village planning scheme was conducted. The calculations indicate that the optimized planning scheme improves overall performance by 36% compared with the original baseline. In conclusion, this study aimed to integrate performance assessment and machine learning algorithms into the decision-making process for optimizing traditional village environments, offering new approaches for sustainable rural development.

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