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

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. D...

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Bibliographic Details
Published in:Sustainability 2021-12, Vol.13 (23), p.13046
Main Authors: Friese, Philipp A., Michalk, Wibke, Fischer, Markus, Hardt, Cornelius, Bogenberger, Klaus
Format: Article
Language:English
Subjects:
Adaptability
Automobiles
Charging
Classification
Clustering
Data acquisition
Demographics
Electric vehicles
Infrastructure
Investigations
Learning algorithms
Machine learning
Municipalities
Sociodemographics
Spatial distribution
Urban environments
Use statistics
User behavior
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Summary: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.
ISSN:2071-1050
2071-1050
DOI:10.3390/su132313046