Urban Green Energy Score Prediction Using XGBoost Based on Climate and Geographic Factors

Abstract

Urbanization has led to significant challenges in energy sustainability, with cities facing growing energy demands and environmental pressures. Traditional models often fail to incorporate complex interactions between climatic and geographic factors, limiting their accuracy in predicting urban energy performance. This research proposes a machine learning-based framework using XGBoost to predict the Urban Green Energy Score, a metric that integrates climate and geographic factors to assess urban energy sustainability. The model leverages data such as temperature, precipitation, land use, and urban structure, offering a comprehensive approach to evaluating energy sustainability. The contributions of this study include the development of an interpretable predictive model, the integration of diverse environmental data, and the application of advanced machine learning techniques. The model is evaluated using performance metrics such as RMSE, MAE, and R², with results demonstrating its effectiveness in predicting energy sustainability across multiple urban environments. Future work could explore incorporating real-time energy consumption data, socioeconomic factors, and deep learning techniques to further improve prediction accuracy and model generalization. This research provides a valuable tool for urban planners to optimize energy consumption and reduce environmental impact, contributing to the development of more sustainable cities.