Time-series Forecasting of Energy Demand and Impact of the COVID-19 Pandemic on Model Performance in Electric Vehicles

Abstract

The increase in environmental problems such as climate change and air pollution caused by global warming has risen the popularity of electric vehicles (EVs) used in the smart grid environment. The increasing number of EVs can affect the grid in terms of power loss and voltage bias by changing the existing demand profile. Effective predicting of EVs energy demand ensures reliability and robustness of grid use, as well as aiding investment planning and resource allocation for charging infrastructures. In this study, the electricity demand amounts in two different cities are modeled by Support Vector Regression, Random Forest, Gauss Process, and Multilayer Perceptron algorithms. The findings reveal that electric vehicle owners usually start to charge their vehicles during the daytime, the COVID-19 pandemic causes a serious decrease in EVs energy demand, and the support vector regression (SVR) is more successful in energy demand forecasting. Furthermore, the results indicate that the decrease in electricity demand during the COVID-19 pandemic caused reduces in the prediction accuracy of the SVR model (decrease of 17.1% in training and 12.6% in test performance, P