ASSESSMENT OF EMPIRICAL MODELS FOR ESTIMATING MEAN MONTHLY GLOBAL SOLAR RADIATION IN KATSINA (FUDMA JOURNAL OF SCIENCES, 2023)
Solar energy occupies the most significant position among the various renewable energy sources in the world today. Global solar radiation data in various locations across the country is not accessible due to unavailability of the required equipment to measure it. This work sought to estimate the mean monthly global solar radiation in Katsina using different empirical models. Daily data of meteorological parameters obtained from the Nigeria meteorological agency (NiMet) was converted into monthly data and fitted using MATLAB curve fitting toolbox to determine the regression coefficients. Models based on temperature, sunshine and hybrid parameters were developed, tested and validated using statistical error indicators such as mean absolute error (MAE), root mean square error (RMSE), mean percentage error (MPE) and coefficient of determination (R2). The prediction results show that the model with the best performance is a temperature-based model with RMSE, MAE, MPE and R2 values of 1.0811, 0.8961, 4.005 and 0.9463 which is within the acceptable prediction error range. However, one of the hybrid parameter–based models also showed good performance with RMSE, MAE, MPE and R2 values of 1.3105, 1.0506, 4.4069 and 0.9280 respectively. Furthermore, the entire prediction results for all the sunshine-based models fitted to the data did not yield acceptable results as the R2 value were not up to 0.5 and the RMSE and MPE were all found to greater than . This could be as a result of the unreliability of the sunshine hour’s data obtained from NiMet. It is recommended that Government, non-governmental organizations and
Keywords: Solar energy, global solar radiation, meteorological parameters, statistical error indicators, empirical models