期刊
TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT
卷 98, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.trd.2021.102969
关键词
Electric bus; Artificial neural network; Energy consumption prediction
资金
- Department of Energy, Office of Energy Efficiency and Renewable Energy (EERE) [DE-EE0008467]
This study developed machine learning models to estimate electric bus energy consumption based on data from Chattanooga in 2019 and 2020, achieving predicted mean absolute percentage error rates of 3% for LSTM and 5% for ANN. The research proposed a data-partitioning algorithm and conducted K-fold cross-validation to select optimal model structures and input variables, demonstrating the predictability of the models when compared with existing literature.
Reliable and accurate estimation of an electric bus's instantaneous energy consumption is critical in evaluating energy impacts of planning and control of electric bus operations. In this study, we developed machine learning-based long short-term memory (LSTM) and artificial neural network (ANN) models to estimate 1 Hz energy consumption of electric buses based on continuous monitoring data of electric buses in Chattanooga, Tennessee, in 2019 and 2020. We propose a data-partitioning algorithm to separate energy charging and discharging modes before applying data-driven estimation models. A K-fold cross-validation-based model selection process was conducted to identify the optimal model structure and input variables in terms of prediction accuracy. The estimation results show the predicted mean absolute percentage error rates of LSTM and ANN models were 3% and 5%, respectively. We compared the proposed models with existing models in the literature based on the same testing data to demonstrate the predictability of our models.
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