Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13091/4765
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dc.contributor.authorErdem, O.E.-
dc.contributor.authorSarucan, A.-
dc.contributor.authorBaysal, M.E.-
dc.date.accessioned2023-11-11T09:03:39Z-
dc.date.available2023-11-11T09:03:39Z-
dc.date.issued2023-
dc.identifier.isbn9783031397769-
dc.identifier.issn2367-3370-
dc.identifier.urihttps://doi.org/10.1007/978-3-031-39777-6_21-
dc.identifier.urihttps://hdl.handle.net/20.500.13091/4765-
dc.descriptionIntelligent and Fuzzy Systems - Intelligence and Sustainable Future Proceedings of the INFUS 2023 Conference -- 22 August 2023 through 24 August 2023 -- -- 299549en_US
dc.description.abstractThis study used statistical data to create predictive models for effective and efficient use of energy generation systems using machine learning algorithms. Four different models predicted wind energy production and compared with actual production data. Model validity and verification were evaluated using mean square error, mean absolute error, and R-squared values. The model constructed with the random forest method is the most successful model. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.en_US
dc.language.isoenen_US
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.relation.ispartofLecture Notes in Networks and Systemsen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDataen_US
dc.subjectEnergyen_US
dc.subjectMachine Learningen_US
dc.subjectForestryen_US
dc.subjectLearning algorithmsen_US
dc.subjectMean square erroren_US
dc.subjectWind poweren_US
dc.subjectWind turbinesen_US
dc.subjectDataen_US
dc.subjectEfficient use of energyen_US
dc.subjectEnergyen_US
dc.subjectEnergy generationsen_US
dc.subjectEnergy productionsen_US
dc.subjectGeneration systemsen_US
dc.subjectMachine learning algorithmsen_US
dc.subjectMachine-learningen_US
dc.subjectPredictive modelsen_US
dc.subjectStatistical datasen_US
dc.subjectMachine learningen_US
dc.titleMachine Learning Prediction of Wind Turbine Energy Productionen_US
dc.typeConference Objecten_US
dc.identifier.doi10.1007/978-3-031-39777-6_21-
dc.identifier.scopus2-s2.0-85172732344en_US
dc.departmentKTÜNen_US
dc.identifier.volume759 LNNSen_US
dc.identifier.startpage173en_US
dc.identifier.endpage180en_US
dc.institutionauthor…-
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.authorscopusid57217209988-
dc.authorscopusid54405086400-
dc.authorscopusid56007700700-
dc.identifier.scopusqualityQ4-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.openairetypeConference Object-
crisitem.author.dept02.09. Department of Industrial Engineering-
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections
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