Application of support vector regression integrated with firefly optimization algorithm for predicting global solar radiation

Abstract

A fundamental factor for proficient designing of solar energy systems is providing precise estimations of the solar radiation. Global solar radiation (GSR) is a vital parameter for designing and operating solar energy systems. Because records of GSR are not available in many places, especially in developing countries, this research aims to model the GSR using support vector regression (SVR) in a hybrid manner that is integrated with the firefly Optimization algorithm (SVR-FFA). For this purpose, the daily meteorological parameters and GSR measured from beginning of 2011 to the end of 2013 at Tabriz synoptic station were utilized. For assessing the performance of the mentioned methods, different statistical indicators were implemented. For all of the defined predictive models with different combinations of meteorological parameters, the performance of the SVR-FFA hybrid model is better than the classical SVR, evidenced by the higher value of R (~0892-0.982 relative to ~0.891-0.977) and lower values of RMSE and MAE (~1.551-3.725vs.1.748-4.067 and ~0.911-2.862vs.1.103-2.742). As a remarkable point studied empirical equations had higher prediction errors comparing with the developed SVR-FFA models. Conclusively, the obtained results proved the high proficiencies of SVR-FFA method for predicting global solar radiation.