A Model For Hourly Solar Radiation Data Generation From Daily Solar Radiation Data Using a Generalized Regression Artificial Neural

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Journal Title, Volume, Page: 
International Journal of Photoenergy Volume 2015 (2015), Article ID 968024, 13 pages http://dx.doi.org/10.1155/2015/968024
Year of Publication: 
Tamer Khatib
University of Klagenfurt, Austria
Current Affiliation: 
Department of Energy engineering and environment, Faculty of Engineering and Information Technology, An-Najah National University, Nablus. Palestine
Wilfried Elmenreich
Preferred Abstract (Original): 
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, sunset hour angle. The output layer has one node which is mean hourly solar radiation. The training and development of the proposed model is 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 show better ability in overcoming the sophistic nature of the solar radiation data.
A_Model_for_Hourly_Solar_Radiation_Data_Generation_from_Daily_Solar_Radiation_Data_Using_a_Generalized_Regression_Artificial_Neural_Network.pdf3.74 MB