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Short-mid-term solar power prediction by using artificial neural networks

Izgi, Ercan; Oztopal, Ahmet; Yerli, Bihter; Kaymak, Mustafa Kemal; Sahin, Ahmet Duran


DataCite XML

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  <identifier identifierType="URL">https://aperta.ulakbim.gov.tr/record/83409</identifier>
  <creators>
    <creator>
      <creatorName>Izgi, Ercan</creatorName>
      <givenName>Ercan</givenName>
      <familyName>Izgi</familyName>
      <affiliation>Yildiz Tech Univ, Dept Elect Engn, Istanbul, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Oztopal, Ahmet</creatorName>
      <givenName>Ahmet</givenName>
      <familyName>Oztopal</familyName>
      <affiliation>Istanbul Tech Univ, Dept Meteorol, Energy Grp, TR-34469 Istanbul, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Yerli, Bihter</creatorName>
      <givenName>Bihter</givenName>
      <familyName>Yerli</familyName>
      <affiliation>Istanbul Tech Univ, Dept Meteorol, Energy Grp, TR-34469 Istanbul, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Kaymak, Mustafa Kemal</creatorName>
      <givenName>Mustafa Kemal</givenName>
      <familyName>Kaymak</familyName>
      <affiliation>Istanbul Tech Univ, Dept Meteorol, Energy Grp, TR-34469 Istanbul, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Sahin, Ahmet Duran</creatorName>
      <givenName>Ahmet Duran</givenName>
      <familyName>Sahin</familyName>
      <affiliation>Istanbul Tech Univ, Dept Meteorol, Energy Grp, TR-34469 Istanbul, Turkey</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Short-Mid-Term Solar Power Prediction By Using Artificial Neural Networks</title>
  </titles>
  <publisher>Aperta</publisher>
  <publicationYear>2012</publicationYear>
  <dates>
    <date dateType="Issued">2012-01-01</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Journal article</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://aperta.ulakbim.gov.tr/record/83409</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1016/j.solener.2011.11.013</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="http://www.opendefinition.org/licenses/cc-by">Creative Commons Attribution</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">Solar irradiation is one of the major renewable energy sources and technologies related with this source have reached to high level applications. Prediction of solar irradiation shows some uncertainties depending on atmospheric parameters such as temperature, cloud amount, dust and relative humidity. These conditions add new uncertainties to the prediction of this astronomical parameter. In this case, prediction of generated electricity by photovoltaic or other solar technologies could be better than directly solar irradiation.</description>
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