Predicting Electric Vehicle Charging Station Availability Using Ensemble Machine Learning

Christopher Hecht, Jan Figgener and Dirk Uwe Sauer
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Christopher Hecht: Institute for Power Electronics and Electrical Drives, RWTH Aachen University, 52066 Aachen, Germany
Jan Figgener: Institute for Power Electronics and Electrical Drives, RWTH Aachen University, 52066 Aachen, Germany
Dirk Uwe Sauer: Institute for Power Electronics and Electrical Drives, RWTH Aachen University, 52066 Aachen, Germany

Energies, 2021, vol. 14, issue 23, 1-24

Abstract: Electric vehicles may reduce greenhouse gas emissions from individual mobility. Due to the long charging times, accurate planning is necessary, for which the availability of charging infrastructure must be known. In this paper, we show how the occupation status of charging infrastructure can be predicted for the next day using machine learning models— Gradient Boosting Classifier and Random Forest Classifier. Since both are ensemble models, binary training data (occupied vs. available) can be used to provide a certainty measure for predictions. The prediction may be used to adapt prices in a high-load scenario, predict grid stress, or forecast available power for smart or bidirectional charging. The models were chosen based on an evaluation of 13 different, typically used machine learning models. We show that it is necessary to know past charging station usage in order to predict future usage. Other features such as traffic density or weather have a limited effect. We show that a Gradient Boosting Classifier achieves 94.8% accuracy and a Matthews correlation coefficient of 0.838, making ensemble models a suitable tool. We further demonstrate how a model trained on binary data can perform non-binary predictions to give predictions in the categories “low likelihood” to “high likelihood”.

Keywords: machine learning; electric vehicles; charging infrastructure; ensemble learning; road transport (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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