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, vol.86, no.2, pp.725-733, 2012 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 86 Issue: 2
  • Publication Date: 2012
  • Doi Number: 10.1016/j.solener.2011.11.013
  • Journal Name: SOLAR ENERGY
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.725-733
  • Yıldız Technical University Affiliated: Yes

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.