African Journal of
Environmental Science and Technology

  • Abbreviation: Afr. J. Environ. Sci. Technol.
  • Language: English
  • ISSN: 1996-0786
  • DOI: 10.5897/AJEST
  • Start Year: 2007
  • Published Articles: 1129

Full Length Research Paper

Analysis of some meteorological parameters using artificial neural network method for Makurdi, Nigeria

Chukwu, S. C.1*, and Nwachukwu, A. N.2
  1Renaissance University, Ugbawka, Enugu, Nigeria. 2University of Manchester, Manchester, United Kingdom.
Email: [email protected]

  •  Accepted: 21 February 2012
  •  Published: 31 March 2012

Abstract

 

The mean daily data for sunshine hours, maximum temperature, cloud cover and relative humidity data, were used to estimate monthly average global solar irradiation on a horizontal surface for Makurdi, Nigeria. The study used artificial neural networks (ANN) for the estimation. Results showed good agreement between the predicted and measured values of global solar irradiation. A correlation coefficient of 0.9982 was obtained with a maximum percentage error (MPE) of 0.8512 and root mean square error (RMSE) of 0.0032. The comparison between the ANN and some existing empirical models showed the advantage of the ANN prediction model.

 

Key words: Sunshine hours, relative humidity, maximum temperature, cloudiness index, global solar radiation