A state of art review on estimation of solar radiation with various models

Basit Öğe Kaydı
dc.authoridBAKIR, Huseyin/0000-0001-5473-5158
dc.authoridyildiz, gokhan/0000-0001-6039-9226
dc.contributor.authorGurel, Ali Etem
dc.contributor.authorAgbulut, Umit
dc.contributor.authorBakir, Huseyin
dc.contributor.authorErgun, Alper
dc.contributor.authorYildiz, Gokhan
dc.date.accessioned2024-09-29T15:57:14Z
dc.date.available2024-09-29T15:57:14Z
dc.date.issued2023
dc.departmentKarabük Üniversitesien_US
dc.description.abstractSolar radiation is free, and very useful input for most sectors such as heat, health, tourism, agriculture, and energy production, and it plays a critical role in the sustainability of biological, and chemical processes in nature. In this framework, the knowledge of solar radiation data or estimating it as accurately as possible is vital to get the maximum benefit from the sun. From this point of view, many sectors have revised their future investments/plans to enhance their profit margins for sustainable development according to the knowledge/estimation of solar radiation. This case has noteworthy attracted the attention of researchers for the estimation of solar radi-ation with low errors. Accordingly, it is noticed that various types of models have been contin-uously developed in the literature. The present review paper has mainly centered on the solar radiation works estimated by the empirical models, time series, artificial intelligence algorithms, and hybrid models. In general, these models have needed the atmospheric, geographic, climatic, and historical solar radiation data of a given region for the estimation of solar radiation. It is seen from the literature review that each model has its advantages and disadvantages in the estimation of solar radiation, and a model that gives the best results for one region may give the worst results for the other region. Furthermore, it is noticed that an input parameter that strongly improves the performance success of the models for a region may worsen the performance success of another region. In this direction, the estimation of solar radiation has been separately detailed in terms of empirical models, time series, artificial intelligence algorithms, and hybrid algorithms. Accord-ingly, the research gaps, challenges, and future directions for the estimation of solar radiation have been drawn in the present study. In the results, it is well-observed that the hybrid models have exhibited more accurate and reliable results in most studies due to their ability to merge between different models for the benefit of the advantages of each model, but the empirical models have come to the fore in terms of ease of use, and low computational costs.en_US
dc.identifier.doi10.1016/j.heliyon.2023.e13167
dc.identifier.issn2405-8440
dc.identifier.issue2en_US
dc.identifier.pmid36747538en_US
dc.identifier.scopus2-s2.0-85147827012en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.heliyon.2023.e13167
dc.identifier.urihttps://hdl.handle.net/20.500.14619/4695
dc.identifier.volume9en_US
dc.identifier.wosWOS:000972990700001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherCell Pressen_US
dc.relation.ispartofHeliyonen_US
dc.relation.publicationcategoryDiğeren_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectSolar radiation estimationen_US
dc.subjectEmpirical methodsen_US
dc.subjectTime series modelsen_US
dc.subjectArtificial neural networksen_US
dc.subjectHybrid modelsen_US
dc.titleA state of art review on estimation of solar radiation with various modelsen_US
dc.typeReviewen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
PubMed İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu