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05-07-2023 | Research Article-Electrical Engineering

A Data-Driven Temporal Charge Profiling of Electric Vehicles

Authors: Dilawar Usman, Khaliq Abdul, Dilawar Asim

Published in: Arabian Journal for Science and Engineering | Issue 11/2023

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Abstract

The article presents a comprehensive study on data-driven temporal charge profiling of electric vehicles (EVs), focusing on the challenges and opportunities in EV load forecasting. It discusses traditional statistical-based forecasting methods and advanced machine learning algorithms, including regression trees and gradient boosting with XGBoost. The research compares the proposed algorithm with eight state-of-the-art ML techniques using three diverse public datasets, demonstrating its superior performance in predicting EV charging patterns. The study highlights the importance of accurate load forecasting for optimizing power grid management and the strategic deployment of charging infrastructure. The novel approach and extensive comparative analysis make this article a valuable resource for professionals in the field of energy and EV technology.

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Metadata
Title
A Data-Driven Temporal Charge Profiling of Electric Vehicles
Authors
Dilawar Usman
Khaliq Abdul
Dilawar Asim
Publication date
05-07-2023
Publisher
Springer Berlin Heidelberg
Published in
Arabian Journal for Science and Engineering / Issue 11/2023
Print ISSN: 2193-567X
Electronic ISSN: 2191-4281
DOI
https://doi.org/10.1007/s13369-023-08036-9

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