Dergi makalesi Açık Erişim

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

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


MARC21 XML

<?xml version='1.0' encoding='UTF-8'?>
<record xmlns="http://www.loc.gov/MARC21/slim">
  <leader>00000nam##2200000uu#4500</leader>
  <datafield tag="909" ind1="C" ind2="4">
    <subfield code="n">2</subfield>
    <subfield code="c">725-733</subfield>
    <subfield code="v">86</subfield>
    <subfield code="p">SOLAR ENERGY</subfield>
  </datafield>
  <controlfield tag="005">20210316061419.0</controlfield>
  <datafield tag="909" ind1="C" ind2="O">
    <subfield code="o">oai:zenodo.org:83409</subfield>
    <subfield code="p">user-tubitak-destekli-proje-yayinlari</subfield>
  </datafield>
  <datafield tag="100" ind1=" " ind2=" ">
    <subfield code="u">Yildiz Tech Univ, Dept Elect Engn, Istanbul, Turkey</subfield>
    <subfield code="a">Izgi, Ercan</subfield>
  </datafield>
  <datafield tag="520" ind1=" " ind2=" ">
    <subfield code="a">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.</subfield>
  </datafield>
  <datafield tag="542" ind1=" " ind2=" ">
    <subfield code="l">open</subfield>
  </datafield>
  <datafield tag="650" ind1="1" ind2="7">
    <subfield code="2">opendefinition.org</subfield>
    <subfield code="a">cc-by</subfield>
  </datafield>
  <datafield tag="540" ind1=" " ind2=" ">
    <subfield code="u">http://www.opendefinition.org/licenses/cc-by</subfield>
    <subfield code="a">Creative Commons Attribution</subfield>
  </datafield>
  <controlfield tag="001">83409</controlfield>
  <datafield tag="980" ind1=" " ind2=" ">
    <subfield code="a">publication</subfield>
    <subfield code="b">article</subfield>
  </datafield>
  <datafield tag="245" ind1=" " ind2=" ">
    <subfield code="a">Short-mid-term solar power prediction by using artificial neural networks</subfield>
  </datafield>
  <datafield tag="260" ind1=" " ind2=" ">
    <subfield code="c">2012-01-01</subfield>
  </datafield>
  <datafield tag="980" ind1=" " ind2=" ">
    <subfield code="a">user-tubitak-destekli-proje-yayinlari</subfield>
  </datafield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="u">Istanbul Tech Univ, Dept Meteorol, Energy Grp, TR-34469 Istanbul, Turkey</subfield>
    <subfield code="a">Oztopal, Ahmet</subfield>
  </datafield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="u">Istanbul Tech Univ, Dept Meteorol, Energy Grp, TR-34469 Istanbul, Turkey</subfield>
    <subfield code="a">Yerli, Bihter</subfield>
  </datafield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="u">Istanbul Tech Univ, Dept Meteorol, Energy Grp, TR-34469 Istanbul, Turkey</subfield>
    <subfield code="a">Kaymak, Mustafa Kemal</subfield>
  </datafield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="u">Istanbul Tech Univ, Dept Meteorol, Energy Grp, TR-34469 Istanbul, Turkey</subfield>
    <subfield code="a">Sahin, Ahmet Duran</subfield>
  </datafield>
  <datafield tag="856" ind1="4" ind2=" ">
    <subfield code="u">https://aperta.ulakbim.gov.trrecord/83409/files/bib-d32d8026-2775-4717-b4d6-2da7cb8233b2.txt</subfield>
    <subfield code="s">169</subfield>
    <subfield code="z">md5:244b923d5463241267264d65079ffa0d</subfield>
  </datafield>
  <datafield tag="024" ind1=" " ind2=" ">
    <subfield code="a">10.1016/j.solener.2011.11.013</subfield>
    <subfield code="2">doi</subfield>
  </datafield>
</record>
55
12
görüntülenme
indirilme
Görüntülenme 55
İndirme 12
Veri hacmi 2.0 kB
Tekil görüntülenme 54
Tekil indirme 12

Alıntı yap