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Charging Point Usage in Germany—Automated Retrieval, Analysis, and Usage Types Explained

Author

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  • Philipp A. Friese

    (Department of Informatics, Technical University of Munich, 85748 Garching, Germany)

  • Wibke Michalk

    (Chair of Traffic Engineering and Control, Technical University of Munich, 80333 Munich, Germany)

  • Markus Fischer

    (Chair of Traffic Engineering and Control, Technical University of Munich, 80333 Munich, Germany)

  • Cornelius Hardt

    (Chair of Traffic Engineering and Control, Technical University of Munich, 80333 Munich, Germany)

  • Klaus Bogenberger

    (Chair of Traffic Engineering and Control, Technical University of Munich, 80333 Munich, Germany)

Abstract

This study presents an approach to collect and classify usage data of public charging infrastructure in order to predict usage based on socio-demographic data within a city. The approach comprises data acquisition and a two-step machine learning approach, classifying and predicting usage behavior. Data is acquired by gathering information on charging points from publicly available sources. The first machine learning step identifies four relevant usage patterns from the gathered data using an agglomerative clustering approach. The second step utilizes a Random Forest Classification to predict usage patterns from socio-demographic factors in a spatial context. This approach allows to predict usage behavior at locations for potential new charging points. Applying the presented approach to Munich, a large city in Germany, results confirm the adaptability in complex urban environments. Visualizing the spatial distribution of the predicted usage patterns shows the prevalence of different patterns throughout the city. The presented approach helps municipalities and charging infrastructure operators to identify areas with certain usage patterns and, hence different technical requirements, to optimize the charging infrastructure in order to help meeting the increasing demand of electric mobility.

Suggested Citation

  • Philipp A. Friese & Wibke Michalk & Markus Fischer & Cornelius Hardt & Klaus Bogenberger, 2021. "Charging Point Usage in Germany—Automated Retrieval, Analysis, and Usage Types Explained," Sustainability, MDPI, vol. 13(23), pages 1-26, November.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:23:p:13046-:d:687592
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    References listed on IDEAS

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    1. Ahmad Almaghrebi & Fares Aljuheshi & Mostafa Rafaie & Kevin James & Mahmoud Alahmad, 2020. "Data-Driven Charging Demand Prediction at Public Charging Stations Using Supervised Machine Learning Regression Methods," Energies, MDPI, vol. 13(16), pages 1-21, August.
    2. Wolbertus, Rick & Kroesen, Maarten & van den Hoed, Robert & Chorus, Caspar, 2018. "Fully charged: An empirical study into the factors that influence connection times at EV-charging stations," Energy Policy, Elsevier, vol. 123(C), pages 1-7.
    3. Andrenacci, N. & Ragona, R. & Valenti, G., 2016. "A demand-side approach to the optimal deployment of electric vehicle charging stations in metropolitan areas," Applied Energy, Elsevier, vol. 182(C), pages 39-46.
    4. Morrissey, Patrick & Weldon, Peter & O’Mahony, Margaret, 2016. "Future standard and fast charging infrastructure planning: An analysis of electric vehicle charging behaviour," Energy Policy, Elsevier, vol. 89(C), pages 257-270.
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