On the global solar radiation prediction methods

The availability of reasonably accurate global solar radiation data is vital for the success of any solar project. However, only a few meteorological stations around the world capture these data as a result of the high cost of measuring equipment and the lack of technical capability in calibrating them. In an attempt to resolve this challenge, engineers and researchers have developed various alternatives to generate the data. In this paper, we surveyed the methods used in generating synthetic global solar radiation with a view to classifying them and bringing out the advantages and the challenges of each. This could motivate the development of a new set of robust prediction techniques that combines the strengths of different existing models for prediction purposes. The various prediction techniques can be generally classified into four categories: the regression techniques, the artificial intelligence methods, the statistical approaches, and the satellite imagery techniques. It is shown from the review that the regression techniques are widely used for the prediction of global solar radiation because of their simplicity. However, their accuracy depends on the completeness of the meteorological data employed in predicting global solar radiation. The statistical methods are based on the assumption that data have an internal linear structure that can be identified and used for prediction purposes. However, it is observed in the literature that the techniques, especially the time series techniques, are generally not good for short time prediction as the error in the prediction of the next value in a series is usually large. Satellite imagery is desirable if surface data for location does not exist. Generally, the use of surface measurements together with a cloud index based on satellite imagery is encouraged to increase the accuracy of prediction. Artificial intelligence methods have been generally favoured for their capability to handle complex relationships between the global solar radiation and the other meteorological data, and as well provide better accuracy and efficiency. This paper is important to engineers and researchers who are interested in the global solar radiation prediction methods. (C) 2016 AIP Publishing LLC.