A Model for Hourly Solar Radiation Data Generation from Daily Solar Radiation Data Using a Generalized Regression Artificial Neural Network. (13th October 2015)

• Record Type: Journal Article
• Authors: Khatib, Tamer; Elmenreich, Wilfried
• Other Names: Van Sark Wilfried G. J. H. M. Academic Editor.
• Is Part Of: International journal of photoenergy. Volume 2015(2015)
• Publication Date: 2015-10-13
• Subjects: Photochemistry -- Periodicals; Photobiology -- Periodicals; Chemistry, Physical and theoretical -- Periodicals; Photochimie; Photobiologie; Chimie physique et théorique; Chemistry, Physical and theoretical; Photobiology; Photochemistry; Electronic journals; Periodicals; 541.35
• DOI: 10.1155/2015/968024
• Languages: English
• ISSNs: 1110-662X

Abstract

Abstract : This paper presents a model for predicting hourly solar radiation data using daily solar radiation averages. The proposed model is a generalized regression artificial neural network. This model has three inputs, namely, mean daily solar radiation, hour angle, and sunset hour angle. The output layer has one node which is mean hourly solar radiation. The training and development of the proposed model are done using MATLAB and 43800 records of hourly global solar radiation. The results show that the proposed model has better prediction accuracy compared to some empirical and statistical models. Two error statistics are used in this research to evaluate the proposed model, namely, mean absolute percentage error and root mean square error. These values for the proposed model are 11.8% and −3.1%, respectively. Finally, the proposed model shows better ability in overcoming the sophistic nature of the solar radiation data.