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

Philipp A. Friese, Wibke Michalk, Markus Fischer, Cornelius Hardt and Klaus Bogenberger
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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

Sustainability, 2021, vol. 13, issue 23, 1-26

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.

Keywords: charging infrastructure; electric mobility; usage types; analysis; automated retrieval; clustering; machine learning; socio-demographic data; usage prediction (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (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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