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

Year 2018, Volume: 2 Issue: 4, 180 - 189, 31.12.2018

Saeed Samadianfard , Salar Jarhan Hamed Sadri Nahand

https://doi.org/10.30521/jes.458328

Cited By: 4

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.

Keywords

Fire fly optimization algorithm, Solar radiation, Statistical parameters, Support vector regression

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