Wind Power Estimation Algorithm Using Artificial Neural Networks Case Study: Eregli

dc.contributor.authorCetinkaya, Nurettin
dc.contributor.authorYapici, Hamza
dc.date.accessioned2020-03-26T18:58:47Z
dc.date.available2020-03-26T18:58:47Z
dc.date.issued2014
dc.departmentSelçuk Üniversitesien_US
dc.description6th International Conference on Electronics, Computers and Artificial Intelligence (ECAI) -- OCT 23-25, 2014 -- Pitesti, ROMANIAen_US
dc.description.abstractBy the global warming and decreasing fossil fuel, alternative energy sources are looked for future and protecting environment. In the recent years, many studies are made about wind power whereby deteriorating environment will be regarded. This study prefers artificial neural network (ANN) algorithm to estimate electrical energy output of wind turbines can be constructed. Although many environmental effects such as wind speed, air density or temperature influence wind turbines installation, ANN estimates electrical energy and power output in the minimum cost. The wind turbine parameters of three manufacturers have been chosen so as to train ANN. For the structure of ANN, 1 hidden layer and 26 neurons have been set. Data in this work have been measured at Eregli terrain in Konya, Turkey. This daily data have been taken between January 2013 and February 2014.en_US
dc.description.sponsorshipIEEE, IEEE Romania sect, IEEE Ind Appl Socen_US
dc.identifier.isbn978-1-4799-5479-7
dc.identifier.issn2378-7147en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12395/31287
dc.identifier.wosWOS:000380489500022en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartofPROCEEDINGS OF THE 2014 6TH INTERNATIONAL CONFERENCE ON ELECTRONICS, COMPUTERS AND ARTIFICIAL INTELLIGENCE (ECAI)en_US
dc.relation.ispartofseriesInternational Conference on Electronics Computers and Artificial Intelligence
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectANNen_US
dc.subjectannual electrical energy estimationen_US
dc.subjectpower plant structureen_US
dc.subjectwind turbineen_US
dc.titleWind Power Estimation Algorithm Using Artificial Neural Networks Case Study: Ereglien_US
dc.typeConference Objecten_US

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