Artificial intelligence approaches to estimate the transport energy demand in Turkey

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dc.authoridEliiyi, Ugur / 0000-0002-5584-891X
dc.authoridTurgut, Mert Sinan / 0000-0002-5739-2119
dc.authoridOner, Erdinc / 0000-0002-0503-7588
dc.authorscopusid56228320400
dc.authorscopusid55246084100
dc.authorscopusid57200158463
dc.authorscopusid12785199900
dc.authorscopusid14521079300
dc.authorwosidEliiyi, Ugur/Q-1810-2019
dc.authorwosidOner, Erdinc/M-4420-2017
dc.contributor.authorTurgut, Mert Sinan
dc.contributor.authorEliiyi, Uğur
dc.contributor.authorTurgut, Oğuz Emrah
dc.contributor.authorÖner, Erdinç
dc.contributor.authorEliiyi, Deniz Türsel
dc.date.accessioned2022-02-15T16:58:45Z
dc.date.available2022-02-15T16:58:45Z
dc.date.issued2021
dc.departmentBakırçay Üniversitesien_US
dc.description.abstractIn this study, eight parameters are selected and their historical data are collected to predict the future of the energy demand of Turkey. The initial eight parameters were the gross domestic product (GDP) of Turkey, average annual US crude oil price (COP), inflation for Turkey in percentages (INF), the population of Turkey, total vehicle travel in kilometers for Turkey, total amount of goods transported on motorways, employment for Turkey, and trade of Turkey. However, after these eight parameters data are analyzed using Pearson and Spearman correlation methods, it is found out that five of these parameters are highly correlated. The remaining three parameters are the GDP of Turkey, COP, and INF for Turkey. Afterward, five separate scenarios are developed to forecast the future of the energy demand of Turkey. The first two scenarios involve the third- and fourth-order polynomial fitting, the third and fourth scenarios employ static and recurrent neural networks, and the fifth scenario utilizes autoregressive models to predict the future energy demand of Turkey. The efficient hybridization of the seagull optimization and very optimistic method of minimization metaheuristic algorithms is carried out to achieve the polynomial fitting of the data. The optimization performance of the hybrid algorithm is assessed by applying the algorithm on benchmark optimization problems and comparing the results with that of some other metaheuristic optimizers. Moreover, it is seen that the forecasts of the first scenario agree well with the Ministry of the Energy and Natural Resources estimates.en_US
dc.identifier.doi10.1007/s13369-020-05108-y
dc.identifier.endpage2476en_US
dc.identifier.issn2193-567X
dc.identifier.issn2191-4281
dc.identifier.issue3en_US
dc.identifier.scopus2-s2.0-85098572971en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage2443en_US
dc.identifier.urihttps://doi.org/10.1007/s13369-020-05108-y
dc.identifier.urihttps://hdl.handle.net/20.500.14034/461
dc.identifier.volume46en_US
dc.identifier.wosWOS:000604546300028en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Heidelbergen_US
dc.relation.journalArabian Journal For Science And Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectSeagull algorithmen_US
dc.subjectVOMMI algorithmen_US
dc.subjectTransport energy demanden_US
dc.subjectTime series predictionen_US
dc.subjectForecastingen_US
dc.subjectOptimization Algorithmen_US
dc.subjectFirefly Algorithmen_US
dc.subjectConsumptionen_US
dc.subjectGdpen_US
dc.subjectPredictionen_US
dc.subjectModelen_US
dc.titleArtificial intelligence approaches to estimate the transport energy demand in Turkeyen_US
dc.typeArticleen_US

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