ML-Based Energy Consumption and Distribution Framework Analysis for EVs and Charging Stations in Smart Grid Environment

Abstract

Electric vehicles (EVs) have become a prominent alternative to fossil fuel vehicles in the modern transportation industry due to their competitive benefits of carbon neutrality and environment friendliness. The tremendous adoption of EVs leads to a significant increase in demand for charging infrastructure. But, the scarcity of charging stations (CSs) concerns efficient and reliable EV charging. Existing studies discussed EV energy consumption prediction schemes at the CS without analyzing the affecting parameters such as energy demand, weather, day, etc. In this regard, we have proposed an energy consumption and distribution framework for EVs in a smart grid environment for efficient EV charging after analyzing the affecting parameters such as location, weekday, weekend, and user. Moreover, we have considered EV dataset to perform a detailed and deep analysis of energy consumption patterns based on the aforementioned parameters such as CS (Station ID) within the location (Location ID), weekday, weekend, and user (UserID). The main aim is to understand the smart grid-based electricity distribution to the CS by analyzing energy consumption patterns for reliable EV charging. We have done different analysis on different parameters and present their graphical representations.

Keywords

• Smart grid
• electric vehicle
• charging station
• energy consumption
• energy distribution
• dataset analysis