Comparison of Incoming Solar Radiation at Different Air Density Regimes Using Neural Network Models

Senkal O.

RUSSIAN METEOROLOGY AND HYDROLOGY, vol.43, no.1, pp.49-55, 2018 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 43 Issue: 1
  • Publication Date: 2018
  • Doi Number: 10.3103/s1068373918010077
  • Journal Name: RUSSIAN METEOROLOGY AND HYDROLOGY
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.49-55
  • Çukurova University Affiliated: Yes

Abstract

This study creates a database on incoming solar radiation using artificial neural networks (ANN) and information on altitude, air temperature and pressure, water vapor pressure, dry air density, water vapor density, and mixing ratio obtained at five weather stations in Turkey. The coefficients of correlation of the calculation results for three regimes of air density with observational data for the training sample (2000-2001) are 99.24%, 99.82%, and 96.67%; for the testing sample (2002), 95.97%, 82.32%, and 95.11%. These values indicate that the usage of artificial neural networks and data of at-mosphere parameters is a correct and effective method for estimation of solar radiation and creation solar databases.