Electricity production based forecasting of greenhouse gas emissions in Turkey with deep learning, support vector machine and artificial neural network algorithms

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dc.contributor.authorBakay, Melahat Sevgul
dc.contributor.authorAgbulut, Umit
dc.date.accessioned2021-12-01T18:47:04Z
dc.date.available2021-12-01T18:47:04Z
dc.date.issued2021
dc.department[Belirlenecek]en_US
dc.description.abstractToday, the world's primary energy demand has been met by the burning of fossil-based fuels at a rate of 85%. This dominant use of fossil-based fuels has led to an accelerating increase in the release of greenhouse gases (GHG) all across the world. The largest share in total GHG emissions belongs to the electricity and heat production sector with a rate of 25%. With this viewpoint, this paper is aiming to forecast the GHG emissions (CO2, CH4, N2O, F-gases, and total GHG) using deep learning (DL), support vector machine (SVM), and artificial neural network (ANN) algorithms from the electricity production sector in Turkey. The dataset is supplied from the Turkish Statistical Institute and covers the years 1990-2018. In the study, the last four years (2015-2018) is being forecasted. To evaluate the performance success of the algorithms, five metrics (RMSE, MBE, rRMSE, R-2, and MAPE) are discussed in detail. In the results, this research is reporting that all algorithms used in the study are giving separately satisfying results for the forecasting of GHG emissions in Turkey. Based on the forecasting results, it is seen that the highest R-2 value for the emissions varies from 0.861 to 0.998 and all results are categorized as excellent in terms of rRMSE (all rRMSE values < 10%). Besides, MBE changes between -2.427 and 2.235, and all MAPE values are smaller than 1.2%. Total GHG emission is forecasted in DL algorithm with very satisfied R-2, RMSE, MBE, rRMSE, and MAPE of 0.998, 2.046, 0.419, 0.406%, and 0.021%, respectively. On the other hand, CO2 accounted for 69.05% of total GHG emissions of Turkey in 1990 but rising by 80.48% in the year 2018. In comparison with those of 1990, electricity production and total GHG emissions of Turkey in 2018 increased by 429.7% and 137.4%, respectively. Total GHG emission corresponding to electricity production is calculated to be 0.3813 Mt-total GHG/MWh in 1990 and 0.1709 Mt-total GHG/MWh in 2018. In conclusion, GHG emissions have recently increased at a high rate, but it is noticed that this increase is considerably higher as compared to the increase in energy production for Turkey. (C) 2020 Elsevier Ltd. All rights reserved.en_US
dc.identifier.doi10.1016/j.jclepro.2020.125324
dc.identifier.issn0959-6526
dc.identifier.issn1879-1786
dc.identifier.scopus2-s2.0-85097710385en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.jclepro.2020.125324
dc.identifier.urihttps://hdl.handle.net/20.500.12684/10125
dc.identifier.volume285en_US
dc.identifier.wosWOS:000611934600022en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Sci Ltden_US
dc.relation.ispartofJournal Of Cleaner Productionen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCO2 emissionen_US
dc.subjectElectricity productionen_US
dc.subjectGHGen_US
dc.subjectGreenhouse gasesen_US
dc.subjectMachine learning algorithmen_US
dc.subjectGlobal Solar-Radiationen_US
dc.subjectCo2 Emissionsen_US
dc.subjectEnergy-Consumptionen_US
dc.subjectRenewable Energyen_US
dc.subjectGhg Emissionsen_US
dc.subjectPredictionen_US
dc.subjectPerformanceen_US
dc.subjectModelsen_US
dc.subjectRegionsen_US
dc.subjectGenerationen_US
dc.titleElectricity production based forecasting of greenhouse gas emissions in Turkey with deep learning, support vector machine and artificial neural network algorithmsen_US
dc.typeArticleen_US

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