Predicting Electric Vehicle Charging Station Availability Using Ensemble Machine Learning
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 pred...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-11-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/14/23/7834 |
| _version_ | 1797507908257185792 |
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| author | Christopher Hecht Jan Figgener Dirk Uwe Sauer |
| author_facet | Christopher Hecht Jan Figgener Dirk Uwe Sauer |
| author_sort | Christopher Hecht |
| collection | DOAJ |
| description | 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”. |
| first_indexed | 2024-03-10T04:55:08Z |
| format | Article |
| id | doaj.art-478c64cf8b85422f895c21c805ec1529 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T04:55:08Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-478c64cf8b85422f895c21c805ec15292023-11-23T02:18:23ZengMDPI AGEnergies1996-10732021-11-011423783410.3390/en14237834Predicting Electric Vehicle Charging Station Availability Using Ensemble Machine LearningChristopher Hecht0Jan Figgener1Dirk Uwe Sauer2Institute for Power Electronics and Electrical Drives, RWTH Aachen University, 52066 Aachen, GermanyInstitute for Power Electronics and Electrical Drives, RWTH Aachen University, 52066 Aachen, GermanyInstitute for Power Electronics and Electrical Drives, RWTH Aachen University, 52066 Aachen, GermanyElectric 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”.https://www.mdpi.com/1996-1073/14/23/7834machine learningelectric vehiclescharging infrastructureensemble learningroad transport |
| spellingShingle | Christopher Hecht Jan Figgener Dirk Uwe Sauer Predicting Electric Vehicle Charging Station Availability Using Ensemble Machine Learning Energies machine learning electric vehicles charging infrastructure ensemble learning road transport |
| title | Predicting Electric Vehicle Charging Station Availability Using Ensemble Machine Learning |
| title_full | Predicting Electric Vehicle Charging Station Availability Using Ensemble Machine Learning |
| title_fullStr | Predicting Electric Vehicle Charging Station Availability Using Ensemble Machine Learning |
| title_full_unstemmed | Predicting Electric Vehicle Charging Station Availability Using Ensemble Machine Learning |
| title_short | Predicting Electric Vehicle Charging Station Availability Using Ensemble Machine Learning |
| title_sort | predicting electric vehicle charging station availability using ensemble machine learning |
| topic | machine learning electric vehicles charging infrastructure ensemble learning road transport |
| url | https://www.mdpi.com/1996-1073/14/23/7834 |
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