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

İZGİ E., öztopal A., Yerli B., KAYMAK M. K., ŞAHİN A. D.

SOLAR ENERGY, cilt.86, sa.2, ss.725-733, 2012 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 86 Sayı: 2
  • Basım Tarihi: 2012
  • Doi Numarası: 10.1016/j.solener.2011.11.013
  • Dergi Adı: SOLAR ENERGY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.725-733
  • Yıldız Teknik Üniversitesi Adresli: Evet

Özet

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.