Reach Us +44-1904-929220


Empirical correlations for the estimation of global solar radiation using meteorological data in WA, Ghana

Monthly average daily global solar radiation data are essential in the design and study of solar energy convention devices. In this study, multiple linear regression models were developed to estimate the monthly average daily global solar radiation using seven parameters during a period of two years from 2010 to 2011 for Wa Polytechnic weather station. The parameters used were the extraterrestrial radiation, mean ambient temperature, mean soil temperature, relative humidity, declination,ratio of the difference between the maximum and minimum monthly mean ambient temperature to the minimum monthly mean ambient temperautre and ratio of sunshine duration. Selected models were compared on the basis of the statistical error tests; mean bias error (MBE), mean percent error (MPE), root mean square error (RMSE) and the t-test. Based on the statistical results, the correlation equation that could be employed for the purposes of estimating global solar radiation of locations that have the same climate, latitude and altitude as Wa Polytechnic weather station is given as 1.350 H = - + 0.007RH + 44.800n N + 2.000sind The present work will help to advance the state of knowledge of global solar radiation to the point where it has applications in the estimation of monthly average daily global solar radiation.

Author(s): Emmanuel. A. Sarsah and Felix. A. Uba

Abstract | PDF

Share this  Facebook  Twitter  LinkedIn  Google+
30+ Million Readerbase
Recommended Conferences
Flyer image
Abstracted/Indexed in
  • Chemical Abstracts Service (CAS)
  • Index Copernicus
  • Google Scholar
  • Genamics JournalSeek
  • China National Knowledge Infrastructure (CNKI)
  • CiteFactor
  • Electronic Journals Library
  • Directory of Research Journal Indexing (DRJI)
  • WorldCat
  • Proquest Summons
  • Publons
  • Serials Union Catalogue (SUNCAT)
  • Geneva Foundation for Medical Education and Research
  • Secret Search Engine Labs