A comparative study between fuzzy linear regression and support vector regression for global solar radiation prediction in Iran

Energy is fundamental to, and plays a prominent role in the quality of life. Sustainable energy is important for the benefits it yields. Sustainable energy technologies are clean sources of energy that have a much lower environmental impact than conventional energy technologies. Among the different forms of clean energy, solar energy has attracted a lot of attention as it is not only sustainable, but is also renewable. Because the number of meteorological stations where global solar radiation (GSR) is recorded is limited in Iran, the aim was to develop three distinctive models in order to prognosticate GSR in Tehran Province, Iran. Accordingly, the fuzzy linear regression (FLR), polynomial and radial basis function (RBF) were applied as the kernel function of support vector regression (SVR). Input energies from different meteorological data obtained from the only station in the study region were selected as the model inputs while GSR was chosen as the model output. Instead of minimizing the observed training error, SVR_poly and SVR_rbf attempted to minimize the generalization error bounds so as to achieve generalized performance. The experimental results show that it is possible to achieve enhanced predictive accuracy and capability of generalization via the proposed approach. The calculated root mean square error and correlation coefficient disclosed that SVR_rbf performed well in predicting GSR compared with FLR.