Research on Energy-Efficient Design of Chinese Building Clusters Based on Digital Twin Technology

This paper constructs a digital twin-based framework for building energy-efficient design, using Nanjing as a case study. The framework integrates Internet of Things (IoT), Building Information Modeling (BIM), big data analytics, and artificial intelligence to enable real-time monitoring and dynamic modeling of energy consumption. Data processing is conducted with Python's GeoPandas and Numpy libraries. The XGBoost and K-Means algorithms are employed to optimize energy consumption prediction and reveal spatial heterogeneity characteristics. The research demonstrates that the model achieves rapid simulation and prediction of urban energy consumption with minimal error, providing valuable energy assessment references for urban planning and facilitating the development of urban energy systems.