Energy Economy Prediction for Electric Buses Using Machine Learning

In recent years, the transition to electric city buses has emerged as a pivotal component of sustainable urban transportation. This paper presents a data-driven approach to predicting the energy consumption and operational efficiency of electric city buses using advanced machine learning techniques. Our analysis employs algorithms such as Random Forest, Support Vector Machines, and Neural Networks, to capture the complexities of urban dynamics and enhance the reliability of energy forecasts. By integrating these predictive models into energy management systems, cities can enhance the performance of their electric bus fleets, reduce operational costs, and contribute to a greener urban environment. This research highlights the potential of machine learning as a transformation tool in the development of a data-driven energy economy for public transportation. This project aims at increased transparency of battery electric buses (BEB) energy economy. Achieving a prediction accuracy of more than 94%, our models performed excellent in combination with the sophisticated selection of features. Key Words: Electric buses, Machine learning, Energy prediction, Neural networks, Sustainable transport, BEB