The Multi-Layered Perceptrons Neural Networks for the Prediction of Daily Solar Radiation
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32769
The Multi-Layered Perceptrons Neural Networks for the Prediction of Daily Solar Radiation

Authors: Radouane Iqdour, Abdelouhab Zeroual

Abstract:

The Multi-Layered Perceptron (MLP) Neural networks have been very successful in a number of signal processing applications. In this work we have studied the possibilities and the met difficulties in the application of the MLP neural networks for the prediction of daily solar radiation data. We have used the Polack-Ribière algorithm for training the neural networks. A comparison, in term of the statistical indicators, with a linear model most used in literature, is also performed, and the obtained results show that the neural networks are more efficient and gave the best results.

Keywords: Daily solar radiation, Prediction, MLP neural networks, linear model

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1060259

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1272

References:


[1] H. Bouhadou, MM. Hassani, A. Zeroual, Wilkinson AJ, "Stochastic Simulation of weather data using higher order statistics", Renewable Energy , 12(1), 1997, pp. 21-37.
[2] S. Safi, A. Zeroual, "MA system identification using higher order cumulants: application to modeling solar radiation", Journal of Statistical Computation and Simulation, Vol.72 (7) , 2002, pp. 533-548,.
[3] S. Safi, A. Zeroual, MM. Hassani, "Prediction of global daily solar radiation using higher order statistics", Renewable Energy, 27, 2002, pp. 647-666.
[4] S. Safi, A. Zeroual, "Modelling solar data using high order statistics" A.M.S.E. Advances in Modelling & Analysis, Vol. 6, N┬░1, 2, Advances D-2001, pp. 1-16..
[5] M. T. Hagan, H. B. Demuth, and M. H. Beale, Neural Network Design, Boston, MA: PWS Publishing, 1996.
[6] J.J. Hopfield "Neural Networks and Physical Systems with Emergent Collective Computational Abilities", Proceeding of the Natl. Acd. Sci., n┬░79, 1982, pp. 25554-2558.
[7] Hornik K., Stinchcombe M., White H. "Multilayer Feedforward Networks are universal Appoximators" Neural Networkd, n┬░2, pp .359- 366.
[8] Soteris A. Kalogirou "Artificial neural networks in renewable energy systems applications: a review" Renewable and Sustainable Energy Reviews 5, 2001, 373-401.
[9] R. Iqdour, A. Zeroual, "Prédiction de l-irradiation journalière ├á l-aide des réseaux de neurones MLP", International Conference on Approximation Methods and numerical Modeling in environment and Natural Resources MAMERN 2005, May 9-11, 2005 Oujda - Morocco.
[10] R. Iqdour, A. Zeroual, "An application of the MLP neural networks to the prediction of daily solar radiation", IVème Conférence Internationale en Recherche Opérationnelle Théorie et Applications CIRO 05, 22-26 Mai 2005 Marrakech - Morocco.
[11] E. Polak, Computational Methods in Optimisation: a Unified Approach, Editions Academic Press.
[12] J. Dayhoff Neural Networks Architectures, Editions Van Norstrand Reynold.
[13] B. Widrow, "Adaline and madaline", Proceedings of the 1st International Conference on Neural Networks, pp.143-158.
[14] T. Tollenaere, "Fast Adaptive Backpropagation with good Scaling Properties" Neural Networks, n┬░3, pp.561-573.
[15] J.S. Armstrong, F. Collopy, "Error Measures for Generalzing About Forecasting Methods: Empirical Comparison ", International Journal of Forecast in, n┬░8, pp.69-80.
[16] D.R.Hush, B.G. Horne, "Progress in Supervised Neural Networks", IEEE Signal Processing Magazine, n┬░10, pp.8-39.
[17] G.E.P. Box, G.M. Jenkins, Time Series Analysis, Forecasting and Control, Editions Holden Day, 1970.