Gelişmiş Arama

Basit öğe kaydını göster

dc.contributor.authorTao, H.
dc.contributor.authorEwees, A.A.
dc.contributor.authorAl-Sulttani, A.O.
dc.contributor.authorBeyaztaş, Ufuk
dc.contributor.authorHameed, M.M.
dc.contributor.authorSalih, S.Q.
dc.date.accessioned2021-06-05T20:01:27Z
dc.date.available2021-06-05T20:01:27Z
dc.date.issued2021
dc.identifier.issn2352-4847
dc.identifier.urihttps://doi.org/10.1016/j.egyr.2020.11.033
dc.identifier.urihttps://hdl.handle.net/20.500.12960/1117
dc.description2-s2.0-85097405752en_US
dc.description.abstractAccurate solar radiation (SR) prediction is one of the essential prerequisites of harvesting solar energy. The current study proposed a novel intelligence model through hybridization of Adaptive Neuro-Fuzzy Inference System (ANFIS) with two metaheuristic optimization algorithms, Salp Swarm Algorithm (SSA) and Grasshopper Optimization Algorithm (GOA) (ANFIS-muSG) for global SR prediction at different locations of North Dakota, USA. The performance of the proposed ANFIS-muSG model was compared with classical ANFIS, ANFIS-GOA, ANFIS-SSA, ANFIS-Grey Wolf Optimizer (ANFIS-GWO), ANFIS-Particle Swarm Optimization (ANFIS-PSO), ANFIS-Genetic Algorithm (ANFIS-GA) and ANFIS-Dragonfly Algorithm (ANFIS-DA). Consistent maximum, mean and minimum air temperature data for nine years (2010–2018) were used to build the models. ANFIS-muSG showed 25.7%–54.8% higher performance accuracy in terms of root mean square error compared to other models at different locations of the study areas. The model developed in this study can be employed for SR prediction from temperature only. The results indicate the potential of hybridization of ANFIS with the metaheuristic optimization algorithms for improvement of prediction accuracy. © 2020 The Authorsen_US
dc.description.sponsorship2020GY-078en_US
dc.description.sponsorshipThe authors acknowledged their appreciation and gratitude to the North Dakota Agricultural Weather Network (NDAWN), for the dataset used in the current research. Also, the authors reveal their highly appreciation to the respected editors and reviewers for their constructive comments on the presented research. Further, we acknowledge the support received from the Key Research and Development Program in Shaanxi Province (2020GY-078).en_US
dc.description.sponsorshipThe authors acknowledged their appreciation and gratitude to the North Dakota Agricultural Weather Network (NDAWN), for the dataset used in the current research. Also, the authors reveal their highly appreciation to the respected editors and reviewers for their constructive comments on the presented research. Further, we acknowledge the support received from the Key Research and Development Program in Shaanxi Province ( 2020GY-078 ).en_US
dc.language.isoengen_US
dc.publisherElsevier Ltden_US
dc.relation.ispartofEnergy Reportsen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectMetaheuristic Algorithmsen_US
dc.subjectNorth Dakotaen_US
dc.subjectOptimizeren_US
dc.subjectRenewable Energyen_US
dc.subjectSolar Radiationen_US
dc.titleGlobal solar radiation prediction over North Dakota using air temperature: Development of novel hybrid intelligence modelen_US
dc.typearticleen_US
dc.authorid0000-0002-0103-1488
dc.departmentİktisadi ve İdari Bilimler Fakültesi, Ekonomi ve Finans Bölümüen_US
dc.contributor.institutionauthorBeyaztaş, Ufuk
dc.identifier.doi10.1016/j.egyr.2020.11.033
dc.identifier.volume7en_US
dc.identifier.startpage136en_US
dc.identifier.endpage157en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US


Bu öğenin dosyaları:

Thumbnail

Bu öğe aşağıdaki koleksiyon(lar)da görünmektedir.

Basit öğe kaydını göster