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TY - THES AU - Hecht, Christopher TI - Usage overview, prediction, and siting optimization for electric vehicles public charging infrastructure with machine learning and big data methods VL - 171 PB - Rheinisch-Westfälische Technische Hochschule Aachen VL - Dissertation CY - Aachen M1 - RWTH-2023-11220 T2 - Aachener Beiträge des ISEA SP - 1 Online-Ressource : Illustrationen PY - 2023 N1 - Veröffentlicht auf dem Publikationsserver der RWTH Aachen University 2024. - Überarbeitete Auflage mit Korrektur von Abbildungen verfügbar N1 - Dissertation, Rheinisch-Westfälische Technische Hochschule Aachen, 2023 AB - Anthropogenic climate change caused by greenhouse gas emissions is among the biggest challenges that humanity must tackle in the 21st century. We must reduce emissions rapidly with the goal of reaching net-zero emissions by the middle of the century as stated in the Paris Agreement. Looking at the last years, mobility stands out as particularly hard to decarbonize. For road traffic, electromobility is a path to drive down emissions at the required scale and speed. To realise this transition to electric drivetrains, charging infrastructure is required. Next to private recharging opportunities, public charging infrastructure is essential to ensure that vehicles can also be recharged while away on the road or while travelling longer distances. This dissertation applies machine learning and big data methods to improve the operation of charging infrastructure. Based on extensive field datasets, we show typical usage patterns for public charging infrastructure such as chargers being occupied predominantly during the day and in urban centres. Alternating current chargers are used more during the week while direct current chargers are used more on the weekend. These patterns are generated by users that can be grouped into the categories “resident”, “commuter”, and “opportunity user”. Commuters charge during the day and more frequently in industrial areas as compared to the other groups. Opportunity chargers also charge mostly during the day but remain at the station for shorter periods and visit stations much less frequently. Both groups drive the demand spike during the day in the urban and industrial areas. In contrast, residents generate demand in urban and suburban environment and lean towards charging in the evening. The combination of the groups leads to distinct patterns in the various area types: Industrial areas experience a single charging peak during the day and mostly on weekdays. Urban areas show a strong peak during the day from commuters and opportunity users working or visiting the areas. A second peak is generated in the early evening from the returning residents. Suburban areas show a similar pattern, but since less people frequent these during the day, the daytime peak is lower as in urban areas. Lastly, uninhabited areas are mostly used by fast-chargers which are practically only used by opportunity users coming in during the day.The machine learning methods introduced in this dissertation are able to predict usage patterns in time and space. For the prediction in time, the tools introduced predict whether an existing station will be occupied to at least 50 LB - PUB:(DE-HGF)11 ; PUB:(DE-HGF)3 DO - DOI:10.18154/RWTH-2023-11220 UR - https://publications.rwth-aachen.de/record/974102 ER -