Home Electric Vehicle Charge Scheduling Using Machine Learning Technique

Abstract

With the help of artificial intelligence and advanced metering infrastructure (AMI), the analysis of electric vehicle integration will play a vital role in the future smart grid. Because getting data from smart appliances, processing that data using advanced techniques to get the desired output in near real-time is going to be a significant advantage of the smart grid. In this paper, a machine learning technique called support vector machine(SVM) is used to analyze the home charge scheduling. With the help of user energy consumption, electric vehicle SOC information at different time intervals, it can predict the status of the electric vehicle, i.e., Idle, Grid to Vehicle(G2V), or Vehicle to Grid(V2G) with close to cent percent accuracy. The results show the advantage of the SVM technique for analysis of home charge scheduling using intermediate EV data.