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dc.contributor.authorŞahin, Utkucan
dc.contributor.authorBallı, Serkan
dc.contributor.authorChen, Yan
dc.date.accessioned2021-08-26T07:14:37Z
dc.date.available2021-08-26T07:14:37Z
dc.date.issued2021en_US
dc.identifier.citation[1]Şahin U, Ballı S, Chen Y. Forecasting seasonal electricity generation in European countries under Covid-19-induced lockdown using fractional grey prediction models and machine learning methods. Applied Energy 2021;302:117540. doi:10.1016/j.apenergy.2021.117540.en_US
dc.identifier.issn03062619
dc.identifier.urihttps://doi.org/10.1016/j.apenergy.2021.117540
dc.identifier.urihttps://hdl.handle.net/20.500.12809/9496
dc.description.abstractBalances in the energy sector have changed since the implementation of the Covid-19 pandemic lockdown in Europe. This paper analyses how the lockdown affected electricity generation in European countries and how it will reshape future energy generation. Monthly electricity generation from total renewables and non-renewables in France, Germany, Spain, Turkey, and the UK from January 2017 to September 2020 were evaluated and compared. Four seasonal grey prediction models and three machine learning methods were used for forecasting; the quarterly results are presented to the end of 2021. Additionally, the share of electricity generation from renewables in total electricity generation from 2017 to 2021 for the selected countries was compared. Electricity generation from total non-renewables in the second quarter of 2020 for France, Germany, Spain, and the UK decreased by 21%–25% compared to the same period of 2019; the decline in Turkey was approximately 11%. Additionally, electricity generation from non-renewables in the third quarter of 2020 for all countries, except Turkey, decreased compared to the same period of the previous year. All grey prediction models and support vector machine method forecast that the share of renewables in total electricity generation will increase continuously in France, Germany, Spain, and the UK to the end of 2021. The forecasting methods provided by this study open new avenues for research on the impact of the Covid-19 pandemic on the future of the energy sector.en_US
dc.item-language.isoengen_US
dc.publisherElsevier Ltden_US
dc.relation.isversionof10.1016/j.apenergy.2021.117540.en_US
dc.item-rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCovid-19en_US
dc.subjectForecastingen_US
dc.subjectElectricity generationen_US
dc.subjectFractional grey modelen_US
dc.subjectMachine learningen_US
dc.subjectSeasonal fluctuationsen_US
dc.titleForecasting seasonal electricity generation in European countries under Covid-19-induced lockdown using fractional grey prediction models and machine learning methodsen_US
dc.item-typearticleen_US
dc.contributor.departmentMÜ, Teknoloji Fakültesi, Enerji Sistemleri Mühendisliği Bölümüen_US
dc.contributor.authorID0000-0002-5869-8451en_US
dc.contributor.authorID0000-0002-4825-139Xen_US
dc.contributor.institutionauthorŞahin, Utkucan
dc.contributor.institutionauthorBallı, Serkan
dc.identifier.volume302en_US
dc.relation.journalApplied Energyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US


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