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Integrated support vector regression and an improved particle swarm optimization-based model for solar radiation prediction

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dc.citedby33
dc.contributor.authorGhazvinian H.en_US
dc.contributor.authorMousavi S.-F.en_US
dc.contributor.authorKarami H.en_US
dc.contributor.authorFarzin S.en_US
dc.contributor.authorEhteram M.en_US
dc.contributor.authorHossain M.S.en_US
dc.contributor.authorFai C.M.en_US
dc.contributor.authorHashim H.B.en_US
dc.contributor.authorSingh V.P.en_US
dc.contributor.authorRos F.C.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorAfan H.A.en_US
dc.contributor.authorLai S.H.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid57209110288en_US
dc.contributor.authorid7003344568en_US
dc.contributor.authorid36863982200en_US
dc.contributor.authorid55315758000en_US
dc.contributor.authorid57113510800en_US
dc.contributor.authorid55579596900en_US
dc.contributor.authorid57214146115en_US
dc.contributor.authorid56800153400en_US
dc.contributor.authorid57211219633en_US
dc.contributor.authorid57222964772en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid56436626600en_US
dc.contributor.authorid36102664300en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2023-05-29T07:25:48Z
dc.date.available2023-05-29T07:25:48Z
dc.date.issued2019
dc.descriptionArticle; case study; genetic algorithm; mathematical computing; process optimization; sensitivity analysis; solar radiation; statistical model; statistical parameters; support vector machine; algorithm; forecasting; human; humidity; regression analysis; solar energy; sunlight; turkey (bird); wind; Algorithms; Forecasting; Humans; Humidity; Regression Analysis; Solar Energy; Sunlight; Support Vector Machine; Turkey; Winden_US
dc.description.abstractSolar energy is a major type of renewable energy, and its estimation is important for decision- makers. This study introduces a new prediction model for solar radiation based on support vector regression (SVR) and the improved particle swarm optimization (IPSO) algorithm. The new version of algorithm attempts to enhance the global search ability for the PSO. In practice, the SVR method has a few parameters that should be determined through a trial-and-error procedure while developing the prediction model. This procedure usually leads to non-optimal choices for these parameters and, hence, poor prediction accuracy. Therefore, there is a need to integrate the SVR model with an optimization algorithm to achieve optimal choices for these parameters. Thus, the IPSO algorithm, as an optimizer is integrated with SVR to obtain optimal values for the SVR parameters. To examine the proposed model, two solar radiation stations, Adana, Antakya and Konya, in Turkey, are considered for this study. In addition, different models have been tested for this prediction, namely, the M5 tree model (M5T), genetic programming (GP), SVR integrated with four different optimization algorithms SVR-PSO, SVR-IPSO, Genetic Algorithm (SVR-GA), FireFly Algorithm (SVR-FFA) and the multivariate adaptive regression (MARS) model. The sensitivity analysis is performed to achieve the highest accuracy level of the prediction by choosing different input parameters. Several performance measuring indices have been considered to examine the efficiency of all the prediction methods. The results show that SVR-IPSO outperformed M5T and MARS. � 2019 Ghazvinian et al.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNoe0217634
dc.identifier.doi10.1371/journal.pone.0217634
dc.identifier.issue5
dc.identifier.scopus2-s2.0-85066505795
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85066505795&doi=10.1371%2fjournal.pone.0217634&partnerID=40&md5=261a81c04082f7656b172db8fbb7804c
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24680
dc.identifier.volume14
dc.publisherPublic Library of Scienceen_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitlePLoS ONE
dc.titleIntegrated support vector regression and an improved particle swarm optimization-based model for solar radiation predictionen_US
dc.typeArticleen_US
dspace.entity.typePublication
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