BOUSSAADA, Zina; CUREA, Octavian; REMACI, Ahmed; CAMBLONG, Haritza; MRABET BELLAAJ, Najiba

doi:10.3390/en11030620

BOUSSAADA, Zina
ESTIA INSTITUTE OF TECHNOLOGY

CUREA, Octavian

ESTIA INSTITUTE OF TECHNOLOGY

REMACI, Ahmed

ESTIA INSTITUTE OF TECHNOLOGY

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Article de revue

Ce document a été publié dans

Energies. 2018-03, vol. 11, n° 3

Résumé en anglais

The solar photovoltaic (PV) energy has an important place among the renewable energy sources. Therefore, several researchers have been interested by its modelling and its prediction, in order to improve the management of the electrical systems which include PV arrays. Among the existing techniques, artificial neural networks have proved their performance in the prediction of the solar radiation. However, the existing neural network models don't satisfy the requirements of certain specific situations such as the one analyzed in this paper. The aim of this research work is to supply, with electricity, a race sailboat using exclusively renewable sources. The developed solution predicts the direct solar radiation on a horizontal surface. For that, a Nonlinear Autoregressive Exogenous (NARX) neural network is used. All the specific conditions of the sailboat operation are taken into account. The results show that the best prediction performance is obtained when the training phase of the neural network is performed periodically.< Réduire

Mots clés en anglais

prediction

solar radiation

clear sky model

cloud cover

Nonlinear Autoregressive Exogenous (NARX)

URI

https://oskar-bordeaux.fr/handle/20.500.12278/173572

DOI

http://dx.doi.org/10.3390/en11030620

Unités de recherche

ESTIA - Recherche

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A Nonlinear Autoregressive Exogenous (NARX) Neural Network Model for the Prediction of the Daily Direct Solar Radiation

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Résumé en anglais

Mots clés en anglais

URI

DOI

Unités de recherche