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dc.contributor.authorKarabiber, A. and Alçin, O.F.
dc.date.accessioned2021-04-08T12:06:53Z
dc.date.available2021-04-08T12:06:53Z
dc.date.issued2019
dc.identifier10.1109/SGCF.2019.8782324
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85070963106&doi=10.1109%2fSGCF.2019.8782324&partnerID=40&md5=c4d7fcd9d694c67d01d59f8d0c2ec223
dc.identifier.urihttp://acikerisim.bingol.edu.tr/handle/20.500.12898/4142
dc.description.abstractPV power estimation is a basic stage in solar system implementations such as missing data forecasting, power flow control and fault detection. Generally, intelligent methods are preferred to estimate PV power since they are more compatible with nonlinear problems. This paper presents a comparison of Extreme Learning Machine (ELM) and Support Vector Machine (SVM) for PV power estimation. Temperature and power measurements obtained from a PV plant in Sanhurfa are employed as data set to test the methods. The results have been analyzed for sunny, mid-cloudy and cloudy days in summer. The results reveal that ELM has a better performance than SVM in terms of the accuracy of PV power estimation. © 2019 IEEE.
dc.language.isoEnglish
dc.source7th International Istanbul Smart Grids and Cities Congress and Fair, ICSG 2019 - Proceedings
dc.titleShort Term PV Power Estimation by means of Extreme Learning Machine and Support Vector Machine


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