An integrated framework utilizing machine learning to accelerate the optimization of energy-efficient urban block forms

Abstract

Urban block form significantly impacts energy and environmental performance. Therefore, optimizing urban block design in the early stages contributes to enhancing urban energy efficiency and environmental sustainability. However, widely used multi-objective optimization methods based on performance simulation face the challenges of high computational loads and low efficiency. This study introduces a framework using machine learning, especially the XGBoost model, to accelerate multi-objective optimization of energy-efficient urban block forms. A residential block in Nanjing serves as the case study. The framework commences with a parametric block form model driven by design variables, focusing on minimizing building energy consumption (EUI), maximizing photovoltaic energy generation (PVE) and outdoor sunlight hours (SH). Data generated through Latin Hypercube Sampling and performance simulations inform the model training. Through training and hyperparameter tuning, XGBoost’s predictive accuracy was validated against artificial neural network (ANN), support vector machine (SVM), and random forest (RF) models. Subsequently, XGBoost replaced traditional performance simulations, conducting multi-objective optimization via the NSGA-II algorithm. Results showcase the framework’s significant acceleration of the optimization process, improving computational efficiency by over 420 times and producing 185 Pareto optimal solutions with improved performance metrics. SHAP analysis highlighted shape factor (SF), building density (BD), and building orientation (BO) as key morphological parameters influencing EUI, PVE, and SH. This study presents an efficient approach to energy-efficient urban block design, contributing valuable insights for sustainable urban development.