Journal
SOLAR ENERGY
Volume 105, Issue -, Pages 91-98Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.solener.2014.04.009
Keywords
Daily global solar radiation prediction; Coral Reefs Optimization algorithm; Extreme Learning Machines
Categories
Funding
- Spanish Ministry of Economy [ECO2010-22065-C03-02]
Ask authors/readers for more resources
This paper discusses the performance of a novel Coral Reefs Optimization Extreme Learning Machine (CRO-ELM) algorithm in a real problem of global solar radiation prediction. The work considers different meteorological data from the radiometric station at Murcia (southern Spain), both from measurements, radiosondes and meteorological models, and fully describes the hybrid CRO-ELM to solve the prediction of the daily global solar radiation from these data. The algorithm is designed in such a way that the ELM solves the prediction problem, whereas the CRO evolves the weights of the neural network, in order to improve the solutions obtained. The experiments carried out have shown that the CRO-ELM approach is able to obtain an accurate prediction of the daily global radiation, better than the classical ELM, and the Support Vector Regression algorithm. (C) 2014 Elsevier Ltd. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available