Daily global solar radiation prediction based on a hybrid Coral Reefs Optimization – Extreme Learning Machine approach

• Published in: Solar energy Vol. 105; pp. 91 - 98
• Main Authors: Salcedo-Sanz, S., Casanova-Mateo, C., Pastor-Sánchez, A., Sánchez-Girón, M.
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.07.2014; Elsevier; Pergamon Press Inc
• Subjects: Applied sciences; Coral reefs; Coral Reefs Optimization algorithm; Daily global solar radiation prediction; Energy; Exact sciences and technology; Extreme Learning Machines; Measurement; Natural energy; Optimization algorithms; Radiation; Regression analysis; Solar energy; Solar radiation; Europe; Spain; Daily global solar radiation prediction; Coral Reefs Optimization algorithm; Extreme Learning Machines; Performance evaluation; Global radiation; Regression analysis; Support vector machine; Neural network; Experimental study; Algorithm; Forecasting; Optimization; Learning; Radiometry; Solar radiation
• ISSN: 0038-092X; 1471-1257
• DOI: 10.1016/j.solener.2014.04.009

•This paper presents a hybrid CRO–ELM algorithm for solar radiation prediction.•Novel predictive meteorological variables are considered in this problem.•The CRO is a novel meta-heuristic search algorithm based on simulation of Coral Reefs.•The Extreme Learning Machine (ELM) is a state of the art method for training neural networks.•The proposed hybrid approach has shown excellent results in real data in Spain. 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.