Global Solar Radiation Prediction for Makurdi, Nigeria Using Feed Forward Backward Propagation Neural Network

Abstract

The optimum design of solar energy systems strongly depends on the accuracy of solar radiation data. However, the availability of accurate solar radiation data is undermined by the high cost of measuring equipment or non-functional ones. This study developed a feed-forward backpropagation artificial neural network model for prediction of global solar radiation in Makurdi, Nigeria (7.7322 N long. 8.5391 E) using MATLAB 2010a Neural Network toolbox. The training and testing data were obtained from the Nigeria metrological station (NIMET), Makurdi. Five meteorological input parameters including maximum and temperature, mean relative humidity, wind speed, and sunshine hour were used, while global solar radiation was used as the output of the network. During training, the root mean square error, correlation coefficient and mean absolute percentage error (%) were 0.80442, 0.9797, and 3.9588, respectively; for testing, a root mean square value, correlation coefficient, and mean absolute percentage error (%) were 0.98831, 0.9784, and 5.561, respectively. These parameters suggest high reliability of the model for the prediction of solar radiation in locations where solar radiation data are not available.