Predicting Urban Green Energy Score Using AdaBoost Regression Based on Environmental Inputs

Abstract

Urban areas are facing growing challenges related to energy consumption, environmental degradation, and climate change. Predicting urban sustainability and energy performance is crucial for effective urban planning and policy-making. This research aims to develop a predictive model for the Urban Green Energy Score using AdaBoost regression, a powerful ensemble learning technique. The model incorporates various environmental factors, such as air quality, land surface temperature, and weather-related parameters, to estimate the energy sustainability of urban areas. The key contributions of this study include the development of a robust prediction framework, the application of AdaBoost regression for sustainable urban energy forecasting, and an empirical evaluation based on real-world datasets. The model was evaluated using metrics such as R², RMSE, and MAE, with results indicating high predictive accuracy and robustness. Future work could focus on integrating real-time data, applying deep learning techniques, and extending the model to other urban environments to further improve prediction accuracy and generalization capabilities.