A feature prediction-based method for energy consumption prediction of electric buses

Due to environmental concerns, electric buses (EBs) are becoming increasingly popular. Predicting the energy consumption of EBs is crucial for the operational management of bus fleets and the development of related infrastructure. This paper proposed a novel method for energy consumption prediction. Firstly, real-world data of 30 EBs covering a time span over one year from 3 bus routes was collected and the features that influence the energy consumption of EBs were selected and analysed. Then, these features were categorized into two groups: directly obtainable and indirectly obtainable, and models were built to predict the latter. Finally, the predicted features were combined with the directly obtainable ones to train an energy consumption prediction model based on Convolutional Neural Network (CNN). Cross validation was applied to evaluate the energy consumption prediction method, which achieved a mean average absolute percentage error (MAPE) of 8.13 %, surpassing other existing researches.