@inproceedings{8b26fadce1294023b98aca49dfd4e4ec,
title = "Machine Learning for Solving Charging Infrastructure Planning: A Comprehensive Review",
abstract = "The ever-growing energy demand accompanied with environmental pollution has initiated a paradigm shift towards Electric Vehicles (EVs) from conventional vehicles. Public acceptance of EVs call for availability of charging infrastructure. Charging infrastructure planning is an intricate process involving various activities such as charging station placement, charging demand prediction, charging scheduling etc and interaction of power distribution as well as road network. In recent years, the advent of machine learning has made data driven approaches popular for solving charging infrastructure planning problem. Consequently, researchers have started using machine learning techniques for solving problems associated with charging infrastructure planning such as charging station placement, charging demand prediction, charging scheduling etc. This work aims to provide a comprehensive review of machine learning applications for solving charging infrastructure planning.",
keywords = "charger, electric vehicle, machine learning, review",
author = "Sanchari Deb",
note = "Funding Information: ACONK EDL GEMENT This research is supported by European Consortium of Informatics and Mathematics (ERCIM) Publisher Copyright: {\textcopyright} 2021 IEEE.; 5th International Conference on Smart Grid and Smart Cities, ICSGSC 2021, ICSGSC 2021 ; Conference date: 18-06-2021 Through 20-06-2021",
year = "2021",
month = jun,
day = "18",
doi = "10.1109/ICSGSC52434.2021.9490407",
language = "English",
isbn = "978-1-6654-2970-2",
series = "International Conference on Smart Grid and Smart Cities (ICSGSC)",
publisher = "IEEE Institute of Electrical and Electronic Engineers",
pages = "16--22",
editor = "Om Malik and Liansong Xiong",
booktitle = "5th International Conference on Smart Grid and Smart Cities, ICSGSC 2021",
address = "United States",
}
