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Mantri, Hrushikesh (2025) A data-driven approach to predict the load profile at an electric vehicle charging station. Master's, Universität Bremen.
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Abstract
This master thesis explores prediction of load profile at charging stations,through data-driven methodologies. This research aims to use charging station usage and geo-spatial data, to predict load profile. The study involves collecting and preprocessing data on EV charging patterns and other influencing factors, followed by feature engineering and data analysis to identify key determinants of load profiles
| Item URL in elib: | https://elib.dlr.de/212704/ | ||||||||
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| Document Type: | Thesis (Master's) | ||||||||
| Title: | A data-driven approach to predict the load profile at an electric vehicle charging station | ||||||||
| Authors: |
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| Date: | 9 January 2025 | ||||||||
| Open Access: | Yes | ||||||||
| Number of Pages: | 65 | ||||||||
| Status: | Published | ||||||||
| Keywords: | Charging Station, Load Profile, Machine-learning, Prediction | ||||||||
| Institution: | Universität Bremen | ||||||||
| Department: | Fachbereich 1 - Physik / Elektrotechnik: Institut für Automatisierungstechnik (IAT) | ||||||||
| HGF - Research field: | Energy | ||||||||
| HGF - Program: | Energy System Design | ||||||||
| HGF - Program Themes: | Digitalization and System Technology | ||||||||
| DLR - Research area: | Energy | ||||||||
| DLR - Program: | E SY - Energy System Technology and Analysis | ||||||||
| DLR - Research theme (Project): | E - Energy System Technology | ||||||||
| Location: | Oldenburg | ||||||||
| Institutes and Institutions: | Institute of Networked Energy Systems | ||||||||
| Deposited By: | Ravanbach, Babak | ||||||||
| Deposited On: | 18 Feb 2025 10:52 | ||||||||
| Last Modified: | 27 Feb 2025 11:42 |
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