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Estimation of Global Solar Radiation at Onitsha with Regression Analysis and Artificial Neural Network Models

Author Affiliations

  • 1 Faculty of Physical Sciences, Industrial Physics Department, Ebonyi State University, Abakaliki, Ebonyi State, NIGERIA

Res. J. Recent Sci., Volume 1, Issue (6), Pages 27-31, June,2 (2012)


Energy plays an important role in determining the conditions in which living matter can exist and continuous steering power for social, economic and technological prospective development. This study is aimed at estimating the global solar radiation on horizontal surface using meteorological parameters of average temperature and relative humidity for a period of eleven years (1996-2006) at Onitsha, Anambra State of Nigeria. Regression analysis and artificial neural network models were employed in the analysis. Validation of the results using error analysis show that one-variable model of relative humidity has MBE=0.0032, RMSE=0.0109, MPE=-0.753 and one-variable of average temperature has MBE=0.087, RMSE=0.3025, MPE=-0.778. Two-variable model of relative humidity and average temperature has MBE=0.350, RMSE=1.214 and MPE=-3.928. While artificial neural network has MBE=0.00024, RMSE=0.0134 and MPE=0.203. Based on the above validation results, it therefore become clear that artificial neural network has better agreement with measured global solar radiation. Hence, should be used for estimation of global solar radiation of Onitsha and other locations with similar climatic factors


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