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Solar radiation under clear-sky conditions provides information about the maximum possible magnitude of the solar resource available at a location of interest. This information is useful for determining the limits of solar energy use in applications such as thermal and electrical energy generation. Measurements of solar irradiance to provide this information are limited by the associated cost. It is therefore of great interest and importance to develop models that generate these data in lieu of measurements. This study focused on four such models: Ineichen-Perez (I-P), European Solar Radiation Atlas model (ESRA), multilayer perceptron neural network (MLPNN) and radial basis function neural network (RBFNN) models. These models were calibrated and tested using solar irradiance data measured at eight different locations in South Africa. The I-P model showed the best performance, recording relative root mean square errors of less than 2% across all hours, months and locations. The performances of the MLPNN and RBFNN were poor when averaged over all stations, but tended to show performance similar to that of the I-P model for some of the stations. The ESRA model showed performance that was in between that of the Artificial Neural Networks and that of the I-P model.

Predicting clear-sky global horizontal irradiance at eight locations in South Africa using four models