A review of data mining and solar power prediction

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dc.contributor.author Colak, Medine
dc.contributor.author Bayindir, Ramazan
dc.contributor.author Yeşilbudak, Mehmet
dc.date.accessioned 2021-08-24T06:45:01Z
dc.date.available 2021-08-24T06:45:01Z
dc.date.issued 2016
dc.identifier.uri http://hdl.handle.net/20.500.11787/4199
dc.description.abstract Solar energy is one of the clean and renewable energy sources that are mostly available in the world. As a result of this situation, there are many research studies done on the solar energy in order to get the maximum solar radiation during the day time, to estimate the solar power generation and to increase the efficiency of solar systems. In this paper, especially, a review of data mining methods employed for solar power prediction in the literature is introduced briefly. Input data, recording intervals, the number of training and test datasets of each startificial neural networksudy are also considered in the review process. It is shown that artificial neural networks are the most preferred methods in order to predict solar power generation. tr_TR
dc.language.iso eng tr_TR
dc.relation.isversionof 10.1109/icrera.2016.7884507 tr_TR
dc.rights info:eu-repo/semantics/openAccess tr_TR
dc.subject Data mining tr_TR
dc.subject Solar power prediction tr_TR
dc.subject Methods tr_TR
dc.subject Review tr_TR
dc.title A review of data mining and solar power prediction tr_TR
dc.type conferenceObject tr_TR
dc.relation.journal IEEE 5th International Conference on Renewable Energy Research and Applications (ICRERA’16) tr_TR
dc.contributor.department Nevşehir Hacı Bektaş Veli Üniversitesi/mühendislik-mimarlık fakültesi/elektrik-elektronik mühendisliği bölümü/kontrol ve kumanda sistemleri anabilim dalı tr_TR
dc.contributor.authorID 52131 tr_TR
dc.contributor.authorID 0000-0002-1562-4479 tr_TR
dc.contributor.authorID 10136 tr_TR
dc.identifier.startpage 1117 tr_TR
dc.identifier.endpage 1121 tr_TR


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