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

Pinar Cihan

doi:10.35377/saucis...1209519

EN

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

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<0.001).

Keywords

References

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Details

Primary Language

English

Subjects

Software Engineering (Other)

Journal Section

Research Article

Authors

Pinar Cihan ^*
0000-0001-7958-7251
Türkiye

Early Pub Date

April 28, 2023

Publication Date

April 30, 2023

Submission Date

November 24, 2022

Acceptance Date

January 19, 2023

Published in Issue

Year 2023 Volume: 6 Number: 1

DOI

https://doi.org/10.35377/saucis...1209519

IZ

https://izlik.org/JA38WA52WB

Cite

RIS / Bibtex

APA

Cihan, P. (2023). Time-series Forecasting of Energy Demand in Electric Vehicles and Impact of the COVID-19 Pandemic on Energy Demand. Sakarya University Journal of Computer and Information Sciences, 6(1), 10-21. https://doi.org/10.35377/saucis...1209519

AMA

1.Cihan P. Time-series Forecasting of Energy Demand in Electric Vehicles and Impact of the COVID-19 Pandemic on Energy Demand. SAUCIS. 2023;6(1):10-21. doi:10.35377/saucis.1209519

Chicago

Cihan, Pinar. 2023. “Time-Series Forecasting of Energy Demand in Electric Vehicles and Impact of the COVID-19 Pandemic on Energy Demand”. Sakarya University Journal of Computer and Information Sciences 6 (1): 10-21. https://doi.org/10.35377/saucis. 1209519.

EndNote

Cihan P (April 1, 2023) Time-series Forecasting of Energy Demand in Electric Vehicles and Impact of the COVID-19 Pandemic on Energy Demand. Sakarya University Journal of Computer and Information Sciences 6 1 10–21.

IEEE

[1]P. Cihan, “Time-series Forecasting of Energy Demand in Electric Vehicles and Impact of the COVID-19 Pandemic on Energy Demand”, SAUCIS, vol. 6, no. 1, pp. 10–21, Apr. 2023, doi: 10.35377/saucis...1209519.

ISNAD

Cihan, Pinar. “Time-Series Forecasting of Energy Demand in Electric Vehicles and Impact of the COVID-19 Pandemic on Energy Demand”. Sakarya University Journal of Computer and Information Sciences 6/1 (April 1, 2023): 10-21. https://doi.org/10.35377/saucis. 1209519.

JAMA

1.Cihan P. Time-series Forecasting of Energy Demand in Electric Vehicles and Impact of the COVID-19 Pandemic on Energy Demand. SAUCIS. 2023;6:10–21.

MLA

Cihan, Pinar. “Time-Series Forecasting of Energy Demand in Electric Vehicles and Impact of the COVID-19 Pandemic on Energy Demand”. Sakarya University Journal of Computer and Information Sciences, vol. 6, no. 1, Apr. 2023, pp. 10-21, doi:10.35377/saucis. 1209519.

Vancouver

1.Pinar Cihan. Time-series Forecasting of Energy Demand in Electric Vehicles and Impact of the COVID-19 Pandemic on Energy Demand. SAUCIS. 2023 Apr. 1;6(1):10-21. doi:10.35377/saucis. 1209519