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

Zina Boussaada (1, 2, 3) , Octavian Curea (3) , Ahmed Remaci (3) , Haritza Camblong (3) , Najiba Mrabet Bellaaj (2)

1 UPV / EHU - University of the Basque Country = Euskal Herriko Unibertsitatea
2 ENIT - Ecole Nationale d'Ingénieurs de Tunis
3 ESTIA INSTITUTE OF TECHNOLOGY

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.

Mots clés: prediction; solar radiation; clear sky model; cloud cover; Nonlinear Autoregressive Exogenous (NARX)

Domaines: Sciences de l'ingénieur [physics]; Energie électrique; Intelligence artificielle [cs.AI]

Zina Boussaada, Octavian Curea, Ahmed Remaci, Haritza Camblong, Najiba Mrabet Bellaaj. A Nonlinear Autoregressive Exogenous (NARX) Neural Network Model for the Prediction of the Daily Direct Solar Radiation. Energies, 2018, 11 (3), ⟨10.3390/en11030620⟩. ⟨hal-01933133⟩