A Machine Learning Framework to identify the Optimal location for Electric Vehicle Charging Stations

Sharma, Nayan (2022) A Machine Learning Framework to identify the Optimal location for Electric Vehicle Charging Stations. Masters thesis, Dublin, National College of Ireland.

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Abstract

Electric Vehicles are a next-generation mode of transportation. An Electric Vehicle charging station (EVCS) is a place that supplies electrical power to the electric vehicle. It is also known as a charge point or electric vehicle supply equipment (EVSE). Electric Vehicle charging stations (EVCS) are required to meet future demand and to solve vehicle charging availability and compatibility issues. However, the challenge is to identify the optimal location for the electric vehicle charging station. This research proposes a framework to predict the optimal location to install Electric Vehicle charging stations. The proposed framework combines machine learning clustering and the regression model with the spatial model. Data sets containing location postcodes with energy utilization, footfall and passerby, car ownership, and traffic details are used to train the machine learning clustering and the regression model. The Spatial model is built to plot the geographical map and point to the optimal location. An experiment is conducted to identify the optimal location. Results demonstrate Linear regression is the best Machine learning regression model to predict the results based on accuracy and loss. All of the models presented in this paper are evaluated based on their accuracy and loss. The findings from this research highlight a generalized model that enables Distribution Network Operators (DNOs) to identify optimal locations and to plan infrastructure for EVs as excessive electrical power requirements caused by EV integration may have a negative impact on the distribution network.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Stynes, Paul UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science T Technology > TE Highway engineering. Roads and pavements T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Electricity Supply Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Tamara Malone
Date Deposited:	25 May 2023 16:59
Last Modified:	25 May 2023 16:59
URI:	https://norma.ncirl.ie/id/eprint/6657

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