Providing an accurate global model for monthly solar radiation forecasting using artificial intelligence based on air quality index and meteorological data of different cities worldwide.

Abstract

This study aims to present an exact model for predicting solar radiation worldwide through a general model. In this study, mean monthly global solar radiation would have been predicted by applying artificial intelligence methods including artificial neural network, adaptive neuro-fuzzy inference system and hybrid genetic algorithm for different cities worldwide. Investigating different models under various situations showed that the adaptive neuro-fuzzy inference system created the most accurate and precise model for predicting solar radiation. Statistics indexes, such as the determination coefficient, mean absolute percentage error, root mean square error and mean bias error, for the best model selected are 0.999, 5.50E-04, 5.90E-05 and 0.425, respectively. It can be claimed that according to the amount of the statistical indexes, which was mentioned above, the provided model has approximately more formidable accuracy and credibility in comparison with other models, which other researchers did.