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International Journal of Photoenergy
Volume 2017 (2017), Article ID 4025283, 13 pages
Research Article

Learning Processes to Predict the Hourly Global, Direct, and Diffuse Solar Irradiance from Daily Global Radiation with Artificial Neural Networks

1Laboratory of Solar Energy and Environment, Faculty of Sciences, University Mohammed V, B.P. 1014, Rabat, Morocco
2Laboratory of Applied Mathematics, Computer Science, Artificial Intelligence and Pattern Recognition, Faculty of Sciences, University Mohammed V, B.P. 1014, Rabat, Morocco

Correspondence should be addressed to Hanae Loutfi; am.ten.s5mu@iftuol.h

Received 20 April 2017; Accepted 2 July 2017; Published 11 October 2017

Academic Editor: Angelo Albini

Copyright © 2017 Hanae Loutfi et al. 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.


This paper presents three different topologies of feed forward neural network (FFNN) models for generating global, direct, and diffuse hourly solar irradiance in the city of Fez (Morocco). Results from this analysis are crucial for the conception of any solar energy system. Especially, for the concentrating ones, as direct component is seldom measured. For the three models, the main input was the daily global irradiation with other radiometric and meteorological parameters. Three years of hourly data were available for this study. For each solar component’s prediction, different combinations of inputs as well as different numbers of hidden neurons were considered. To evaluate these models, the regression coefficient (R2) and normalized root mean square error (nRMSE) were used. The test of these models over unseen data showed a good accuracy and proved their generalization capability (nRMSE = 13.1%, 9.5%, and 8.05% and R = 0.98, 0.98, and 0.99) for hourly global, hourly direct, and daily direct radiation, respectively. Different comparison analyses confirmed that (FFNN) models surpass other methods of estimation. As such, the proposed models showed a good ability to generate different solar components from daily global radiation which is registered in most radiometric stations.