Strategic forecasting of renewable energy production for sustainable electricity supply: A machine learning approach considering environmental, economic, and oil factors in Turkiye

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dc.authorid0000-0003-0924-3685
dc.authorid0000-0002-4795-1028
dc.contributor.authorAtalan, Yasemin Ayaz
dc.contributor.authorSahin, Hasan
dc.contributor.authorKeskin, Abdulkadir
dc.contributor.authorAtalan, Abdulkadir
dc.date.accessioned2026-02-03T12:00:25Z
dc.date.available2026-02-03T12:00:25Z
dc.date.issued2025
dc.departmentÇanakkale Onsekiz Mart Üniversitesi
dc.description.abstractProviding electricity needs from renewable energy sources is an important issue in the energy policies of countries. Especially changes in energy usage rates make it necessary to use renewable energy resources to be sustainable. The electricity usage rate must be estimated accurately to make reliable decisions in strategic planning and future investments in renewable energy. This study aims to accurately estimate the renewable energy production rate to meet T & uuml;rkiye's electricity needs from renewable energy sources. For this purpose, well-known Machine Learning (ML) algorithms such as Random Forest (RF), Adaptive Boosting (AB), and Gradient Boosting (GB) were utilized. In obtaining forecast data, 15 variables were considered under the oil resources, environmental parameters, and economic factors which are the main parameters affecting renewable energy usage rates. The RF algorithm performed best with the lowest mean absolute percentage error (MAPE, 0.084%), mean absolute error (MAE, 0.035), root mean square error (RMSE, 0.063), and mean squared error (MSE, 0.004) values in the test dataset. The R-2 value of this model is 0.996% and the MAPE value is calculated lower than 10%. The AB model, on the other hand, has the highest error values in the test data set, but still provides an acceptable prediction accuracy. The R-2 value was 0.792% and the MAPE value (0.371%) of this model was calculated to be in the range of 20% < MAPE <= 50%. This study, with its proposed forecasting models, makes significant contributions to energy policies to develop appropriate policies only for planning the amount of electricity usage needed in the future. In this context, this study emphasizes that renewable energy-based electricity generation transformation should be considered as an important strategic goal in terms of both environmental sustainability and energy security.
dc.identifier.doi10.1371/journal.pone.0328290
dc.identifier.issn1932-6203
dc.identifier.issue8
dc.identifier.pmid40763145
dc.identifier.scopus2-s2.0-105012374421
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1371/journal.pone.0328290
dc.identifier.urihttps://hdl.handle.net/20.500.12428/34604
dc.identifier.volume20
dc.identifier.wosWOS:001544822200004
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherPublic Library Science
dc.relation.ispartofPlos One
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20260130
dc.subjectSelection
dc.subjectSolar
dc.subjectResources
dc.subjectEnsembles
dc.subjectPolicy
dc.titleStrategic forecasting of renewable energy production for sustainable electricity supply: A machine learning approach considering environmental, economic, and oil factors in Turkiye
dc.typeArticle

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