Forecasting long-term world annual natural gas production by machine learning

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dc.authoridGunay, M. Erdem/0000-0003-1282-718X|Sen, Doruk/0000-0003-3353-5952
dc.authorwosidSen, Doruk/D-4547-2016
dc.contributor.authorSen, Doruk
dc.contributor.authorHamurcuoglu, K. Irem
dc.contributor.authorErsoy, Melisa Z.
dc.contributor.authorTunc, K. M. Murat
dc.contributor.authorGunay, M. Erdem
dc.date.accessioned2024-07-18T20:56:04Z
dc.date.available2024-07-18T20:56:04Z
dc.date.issued2023
dc.departmentİstanbul Bilgi Üniversitesien_US
dc.description.abstractThe goal of this study is to model the global annual natural gas production using a variety of machine learning models in order to predict future production and determine a peak production date. World gross domestic product (GDP) based on purchasing power parities (at PPPs), inflation percentage, Henry Hub Price, Eum Price, cumulative natural gas resources, and annually discovered new resources were taken as descriptor variables, and Shapley analysis was conducted to observe the importance of features on the dataset. It was revealed according to this analysis that, Henry Hub price, inflation percentage, and newly discovered resources had minor effects on natural gas production, so they were left out. Then, a variety of machine learning algorithms were employed and the one with the highest prediction ability was found to be the stochastic gradient descent (SGD) algorithm. Next, this model was tested under four different scenarios, each with different GDP and natural gas price projections. Finally, natural gas production was found to reach its peak sometime between 2034 and 2046. It was then concluded that rather than relying on a traditional approach based on the Hubbert Curve, a machine learning model that takes into account all relevant factors can be used to accurately forecast natural gas production and its peak time, allowing governments and policymakers to make the necessary preparations.en_US
dc.description.sponsorshipIstanbul Bilgi University [AK 85 073]en_US
dc.description.sponsorshipThe financial support provided by Istanbul Bilgi University Research Fund Project AK 85 073 is gratefully acknowledged.en_US
dc.identifier.doi10.1016/j.resourpol.2022.103224
dc.identifier.issn0301-4207
dc.identifier.issn1873-7641
dc.identifier.scopus2-s2.0-85144048793en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.resourpol.2022.103224
dc.identifier.urihttps://hdl.handle.net/11411/8851
dc.identifier.volume80en_US
dc.identifier.wosWOS:000901671700011en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Sci Ltden_US
dc.relation.ispartofResources Policyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectPeak Oilen_US
dc.subjectHubbert Modelen_US
dc.subjectSupport Vector Regressionen_US
dc.subjectStochastic Gradient Descenten_US
dc.subjectShapley Analysisen_US
dc.subjectAutoregressive Time-Seriesen_US
dc.subjectConsumptionen_US
dc.subjectOilen_US
dc.subjectProjectionen_US
dc.subjectDemanden_US
dc.titleForecasting long-term world annual natural gas production by machine learningen_US
dc.typeArticleen_US

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