The New Prediction Methodology for CO2 Emission to Ensure Energy Sustainability with the Hybrid Artificial Neural Network Approach

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dc.authoridAksu, Inayet Ozge/0000-0002-0963-2982
dc.authoridDemirdelen, Tugce/0000-0002-1602-7262
dc.contributor.authorAksu, Inayet Ozge
dc.contributor.authorDemirdelen, Tugce
dc.date.accessioned2025-01-06T17:36:48Z
dc.date.available2025-01-06T17:36:48Z
dc.date.issued2022
dc.description.abstractEnergy is one of the most fundamental elements of today's economy. It is becoming more important day by day with technological developments. In order to plan the energy policies of the countries and to prevent the climate change crisis, CO2 emissions must be under control. For this reason, the estimation of CO2 emissions has become an important factor for researchers and scientists. In this study, a new hybrid method was developed using optimization methods. The Shuffled Frog-Leaping Algorithm (SFLA) algorithm has recently become the preferred method for solving many optimization problems. SFLA, a swarm-based heuristic method, was developed in this study using the Levy flight method. Thus, the speed of reaching the optimum result of the algorithm has been improved. This method, which was developed later, was used in a hybrid structure of the Firefly Algorithm (FA). In the next step, a new Artificial Neural Network (ANN)-based estimation method is proposed using the hybrid optimization method. The method was used to estimate the amount of CO2 emissions in Turkiye. The proposed hybrid model had the RMSE error 5.1107 and the R2 0.9904 for a testing dataset, respectively. In the last stage, Turkiye's future CO2 emission estimation is examined in three different scenarios. The obtained results show that the proposed estimation method can be successfully applied in areas requiring future estimation.
dc.description.sponsorshipScientific Project Unit of Adana Alparslan Turkes Science and Technology University; [21103008]
dc.description.sponsorshipThis research received no external funding.
dc.identifier.doi10.3390/su142315595
dc.identifier.issn2071-1050
dc.identifier.issue23
dc.identifier.scopus2-s2.0-85143598961
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/su142315595
dc.identifier.urihttps://hdl.handle.net/20.500.14669/2011
dc.identifier.volume14
dc.identifier.wosWOS:000896255300001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofSustainability
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241211
dc.subjectcarbon dioxide emissions
dc.subjectestimation
dc.subjectoptimization
dc.subjectenergy
dc.subjectgreen deal
dc.subjectmetaheuristic algorithms
dc.subjectartificial neural network
dc.titleThe New Prediction Methodology for CO2 Emission to Ensure Energy Sustainability with the Hybrid Artificial Neural Network Approach
dc.typeArticle

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