Solar radiation prediction based on particle swarm optimization and evolutionary algorithm using recurrent neural networks

Abstract

Over the last decade, there has been emphasis on the reduction of the dependency of fossil fuels that resulting in the growth of renewable energy industries. These industries have been significant economic drivers in many parts of the United States supported by both government and private sectors. As a part of renewable energy industries, there is a strong growth in solar power generation industries that often requires prediction of solar energy to develop highly efficient stand-alone photovoltaic systems as well as hybrid power systems. Specifically solar radiation prediction is a important component in the solar energy production. However, some computational intelligence methods that have most successful applications on time series prediction have not yet been investigated on solar radiation prediction. Only a limited number of neural networks models were applied to the solar radiation monitoring. Therefore, we propose an Elman style based recurrent neural network to predict solar radiation from the past solar radiation and solar energy in this research. A hybrid learning algorithm incorporating particle swarm optimization and evolutional algorithm was presented, which takes the complementary advantages of the two global optimization algorithms. The neural networks model was trained by particle swarm optimization and evolutional algorithm to forecast the solar radiation. The excellent experimental results demonstrated that the proposed hybrid learning algorithm can be successfully used for the recurrent neural networks based prediction model for the solar radiation monitoring.