Time-series Forecasting of Energy Demand in Electric Vehicles and Impact of the COVID-19 Pandemic on Energy Demand
No Thumbnail Available
Date
2023
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Access Rights
info:eu-repo/semantics/openAccess
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 EV’s 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 Boulder and Perth cities are modeled by Support Vector Regression, Random Forest, Gauss Process, and Multilayer Perceptron algorithms. In addition, the impact of the COVID-19 pandemic on energy demand in electric vehicles and the energy demand behavior of EV owners were analyzed. The findings reveal that electric vehicle owners usually start to charge their vehicles during the daytime, the COVID-19 pandemic causes a severe decrease in EVs energy demand, and the support vector regression (SVR) is more successful in energy demand forecasting. Furthermore, the results indicate that the decline in electricity demand during the COVID- 19 pandemic caused reductions in the prediction accuracy of the SVR model (a decrease of 17.1% in training and 12.6% in test performance, P<0.001).
Description
Keywords
COVID-19, machine learning, electric vehicles, time series, energy demand
Journal or Series
Sakarya University Journal of Computer and Information Sciences (Online)
WoS Q Value
Scopus Q Value
Volume
6
Issue
1
