Predicting world electricity generation by sources using different machine learning algorithms

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dc.contributor.authorÖzdemir, Mehmet Hakan
dc.contributor.authorAylak, Batin Latif
dc.contributor.authorOral, Okan
dc.contributor.authorİnce, Murat
dc.date.accessioned2025-02-06T15:18:03Z
dc.date.available2025-02-06T15:18:03Z
dc.date.issued2024
dc.departmentTAÜ, Mühendislik Fakültesi, Endüstri Mühendisliği Bölümüen_US
dc.description.abstractElectrical energy plays a crucial role in both social and economic growth. It is thought to be an essential part of industrial manufacturing. In addition to its contribution to industry, electrical energy is essential for addressing the needs of people on a daily basis. Therefore, electricity generation prediction is crucial for accurate electricity planning and energy usage, with machine learning (ML) algorithms becoming popular for their ability to extract complex relationships and make precise predictions. With the data from the period 2000-2022, this study predicts world electricity generation for 2023 by different energy sources employing seven different ML algorithms, namely long short-term memory (LSTM), artificial neural network (ANN), linear regression (LR), support vector regression (SVR), decision tree regression (DTR), random forest regression (RFR) and eXtreme gradient boosting (XGBoost). The algorithms were also contrasted in the study, and it was discovered that LSTM produced the most accurate predictions. [Received: June 16, 2023; Accepted: August 19, 2023]
dc.identifier.citationÖzdemir, Mehmet H., Aylak, Batin L., Oral, O., İnce, M. (2024). Predicting world electricity generation by sources using different machine learning algorithms. International Journal of Oil, Gas and Coal Technology, 35 (1), 98-115.
dc.identifier.doi10.1504/IJOGCT.2024.136028
dc.identifier.endpage115en_US
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85182904204
dc.identifier.startpage98en_US
dc.identifier.urihttps://www.inderscienceonline.com/doi/abs/10.1504/IJOGCT.2024.136028
dc.identifier.urihttps://hdl.handle.net/20.500.12846/1527
dc.identifier.volume35en_US
dc.identifier.wosWOS:001143097800002
dc.indekslendigikaynakWeb of Science
dc.language.isoen
dc.relation.ispartofInternational Journal of Oil, Gas and Coal Technology
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.subjectEnergyen_US
dc.subjectElectricity generationen_US
dc.subjectMachine learningen_US
dc.subjectPredictionen_US
dc.subjectLong short-term memoryen_US
dc.subjectLSTMen_US
dc.subjectArtificial neural networken_US
dc.subjectANNen_US
dc.subjectSupport vector regressionen_US
dc.subjectSVRen_US
dc.subjectDecision tree regressionen_US
dc.subjectDTRen_US
dc.subjectRandom forest regressionen_US
dc.subjectRFRen_US
dc.titlePredicting world electricity generation by sources using different machine learning algorithms
dc.typeArticle

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