General Information
    • ISSN: 1793-821X (Print)
    • Abbreviated Title: J. Clean Energy Technol.
    • Frequency: Quarterly (2013-2014); Bimonthly (Since 2015)
    • DOI: 10.18178/JOCET
    • Editor-in-Chief: Prof. Haider F. Abdul Amir
    • Executive Editor: Ms. Jennifer Zeng
    • Abstracting/ Indexing: EI (INSPEC, IET), Electronic Journals Library, Chemical Abstracts Services (CAS), Ulrich's Periodicals Directory, Google Scholar, ProQuest.
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School of Science and Technology Universiti Malaysia Sabah, Malaysia.
I would like to express my appreciation to all authors, reviewers and editors.

JOCET 2013 Vol.1 (2): 148-150 ISSN: 1793-821X
DOI: 10.7763/JOCET.2013.V1.35

Estimate of Global Solar Radiation by Using Artificial Neural Network in Qena, Upper Egypt

Emad A. Ahmed and M. El-Nouby Adam
Abstract—This paper explores the possibility of developing a prediction model using artificial neural networks (ANNs), which could be used to estimate monthly average daily global solar radiation in Qena, upper Egypt. Results from the paper have shown good agreement between the estimated and measured values of global solar irradiation. A correlation coefficient of 0.998 was obtained with mean bias error (MBE) of 48 Wh/m2 and root mean square error (RMBE) of 115 Wh/m2. The comparison between the ANN and empirical model emphasized the superiority of the proposed ANN prediction model. The application of the proposed ANN model can be extended to other locations with similar climate and terrain.

Index Terms—Artificial neural network, global solar radiation, sunshine duration.

The authors are with Physics Department, Faculty of Sciences, South Valley University, Qena, Egypt (e-mail:;


Cite:Emad A. Ahmed and M. El-Nouby Adam, "Estimate of Global Solar Radiation by Using Artificial Neural Network in Qena, Upper Egypt," Journal of Clean Energy Technologies vol. 1, no. 2, pp. 148-150, 2013.

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