Electric Vehicle Presence Discovery

The global popularization of electric vehicles (EVs) poses an opportunity for the construction of micro-grid and smart community within energy internet on competent the massive and concentrated energy switching and routing in the local environment. Smart EV charging is a promising solution to manage EV charging load that relies on an accurate prediction of EV charging demands. Evaluation of household EV charging demand is a primary factor for designing smart EV charging solutions on household and neighbourhood level. However, this subject has not been adequately discussed in the research community. In this paper, several widely used machine learning algorithms are adopted to forecast the household electric vehicle presence situation such as Logistic Regression, Naive Bayes, Random Forest and XGBoost Classifier. The performances of the algorithms are evaluated and compared. A time series model called ARIMA is also proposed, that performed successfully with forecasting the future electric usage of individual family and to judge whether the family needs smart charging solution in the future or not. By using the above machine learning model, we can have a general understanding of the advantage and disadvantage of each model and when should it be used