Impact of Environmental Conditions on Renewable Energy Prediction: An Investigation Through Tree-Based Community Learning

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dc.authoridAKSOZ, Ahmet/0000-0002-2563-1218
dc.authoridDOGAN, FERDI/0000-0002-9203-697X
dc.authoridoyucu, saadin/0000-0003-3880-3039
dc.contributor.authorDogan, Ferdi
dc.contributor.authorOyucu, Saadin
dc.contributor.authorUnsal, Derya Betul
dc.contributor.authorAksoz, Ahmet
dc.contributor.authorVafaeipour, Majid
dc.date.accessioned2025-05-04T16:45:48Z
dc.date.available2025-05-04T16:45:48Z
dc.date.issued2025
dc.departmentSivas Cumhuriyet Üniversitesi
dc.description.abstractThe real-time prediction of energy production is essential for effective energy management and planning. Forecasts are essential in various areas, including the efficient utilization of energy resources, the provision of energy flexibility services, decision-making amidst uncertainty, the balancing of supply and demand, and the optimization of online energy systems. This study examines the use of tree-based ensemble learning models for renewable energy production prediction, focusing on environmental factors such as temperature, pressure, and humidity. The study's primary contribution lies in demonstrating the effectiveness of the bagged trees model in reducing overfitting and achieving higher accuracy compared to other models, while maintaining computational efficiency. The results indicate that less sophisticated models are inadequate for accurately representing complex datasets. The results evaluate the effectiveness of machine learning methods in delivering valuable insights for energy sectors managing environmental conditions and predicting renewable energy sources
dc.description.sponsorshipEuropean Union's Horizon Europe research and innovation program; [101172877]
dc.description.sponsorshipThis work was supported in part by the European Union's Horizon Europe research and innovation program under grant number: 101172877.
dc.identifier.doi10.3390/app15010336
dc.identifier.issn2076-3417
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85214580772
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/app15010336
dc.identifier.urihttps://hdl.handle.net/20.500.12418/35210
dc.identifier.volume15
dc.identifier.wosWOS:001393450000001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMDPI
dc.relation.ispartofApplied Sciences-Basel
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250504
dc.subjectenvironmental factors
dc.subjectrenewable energy sources
dc.subjectmachine learning
dc.subjectEL
dc.subjectdecision tree
dc.titleImpact of Environmental Conditions on Renewable Energy Prediction: An Investigation Through Tree-Based Community Learning
dc.typeArticle

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