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International Journal of Photoenergy
Volume 2015 (2015), Article ID 968024, 13 pages
http://dx.doi.org/10.1155/2015/968024
Research Article

A Model for Hourly Solar Radiation Data Generation from Daily Solar Radiation Data Using a Generalized Regression Artificial Neural Network

1Department of Energy Engineering and Environment, An-Najah National University, Nablus, State of Palestine
2Institute of Networked and Embedded Systems, University of Klagenfurt, 9020 Klagenfurt, Austria

Received 29 June 2015; Accepted 13 September 2015

Academic Editor: Wilfried G. J. H. M. Van Sark

Copyright © 2015 Tamer Khatib and Wilfried Elmenreich. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Citations to this Article [3 citations]

The following is the list of published articles that have cited the current article.

  • Ravinesh C. Deo, Xiaohu Wen, and Feng Qi, “A wavelet-coupled support vector machine model for forecasting global incident solar radiation using limited meteorological dataset,” Applied Energy, vol. 168, pp. 568–593, 2016. View at Publisher · View at Google Scholar
  • T.V. Dixit, Anamika Yadav, and S. Gupta, “Annual Optimum Tilt Angle Prediction of Solar Collector using PSO Estimator,” IOP Conference Series: Materials Science and Engineering, vol. 225, pp. 012296, 2017. View at Publisher · View at Google Scholar
  • Ravinesh C. Deo, and Mehmet Şahin, “Forecasting long-term global solar radiation with an ANN algorithm coupled with satellite-derived (MODIS) land surface temperature (LST) for regional locations in Queensland,” Renewable and Sustainable Energy Reviews, vol. 72, pp. 828–848, 2017. View at Publisher · View at Google Scholar