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

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dc.contributor.authorCihan, Pınar
dc.date.accessioned2024-10-29T17:53:17Z
dc.date.available2024-10-29T17:53:17Z
dc.date.issued2023
dc.departmentTekirdağ Namık Kemal Üniversitesi
dc.description.abstractThe 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).
dc.identifier.doi10.35377/saucis...1209519
dc.identifier.endpage21
dc.identifier.issn2636-8129
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85198471336
dc.identifier.startpage10
dc.identifier.trdizinid1167850
dc.identifier.urihttps://doi.org/10.35377/saucis...1209519
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/1167850
dc.identifier.urihttps://hdl.handle.net/20.500.11776/13497
dc.identifier.volume6
dc.indekslendigikaynakTR-Dizin
dc.language.isoen
dc.relation.ispartofSakarya University Journal of Computer and Information Sciences (Online)
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectCOVID-19
dc.subjectmachine learning
dc.subjectelectric vehicles
dc.subjecttime series
dc.subjectenergy demand
dc.titleTime-series Forecasting of Energy Demand in Electric Vehicles and Impact of the COVID-19 Pandemic on Energy Demand
dc.typeArticle

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