Solar Power Prediction using Regression Models

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dc.contributor.authorErten, Mustafa Yasin
dc.contributor.authorAydilek, Hüseyin
dc.date.accessioned2025-01-21T14:28:48Z
dc.date.available2025-01-21T14:28:48Z
dc.date.issued2022
dc.description.abstractSolar power prediction is an important problem that has gained significant attention in recent years due to the increasing demand for renewable energy sources. In this paper, we present the results of using four different regression models for solar power prediction: linear regression, logistic regression, Lasso regression, and elastic regression. Our results show that all four models are able to accurately predict solar power, but Lasso regression and elastic regression outperform linear and logistic regression in terms of predicting the maximum solar power output. We also discuss the advantages and disadvantages of each model in the context of solar power prediction.
dc.identifier.dergipark1100957
dc.identifier.doi10.29137/umagd.1100957
dc.identifier.issn1308-5514
dc.identifier.issue3-333
dc.identifier.startpage342
dc.identifier.urihttps://dergipark.org.tr/tr/download/article-file/2364053
dc.identifier.urihttps://dergipark.org.tr/tr/pub/umagd/issue/74185/1100957
dc.identifier.urihttps://doi.org/10.29137/umagd.1100957
dc.identifier.urihttps://hdl.handle.net/20.500.12587/20361
dc.identifier.volume1
dc.language.isoen
dc.publisherKırıkkale Üniversitesi
dc.relation.ispartofUluslararası Mühendislik Araştırma ve Geliştirme Dergisi
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241229
dc.subjectSolar power prediction
dc.subjectregression models
dc.subjectlasso regression
dc.subjectmachine learning
dc.subjectElectrical Engineering
dc.subjectElektrik Mühendisliği
dc.titleSolar Power Prediction using Regression Models
dc.typeArticle

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