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

• Published in: Energies (Basel) Vol. 11; no. 3; p. 620
• Main Authors: Boussaada, Zina, Curea, Octavian, Remaci, Ahmed, Camblong, Haritza, Mrabet Bellaaj, Najiba
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.03.2018; MDPI
• Subjects: Artificial Intelligence; clear sky model; cloud cover; Computer Science; Electric power; Energy sources; Engineering Sciences; Neural networks; Nonlinear Autoregressive Exogenous (NARX); Photovoltaics; prediction; Renewable energy sources; Sailing & sailboats; Solar cells; Solar energy; Solar radiation; prediction; cloud cover; solar radiation; clear sky model; Nonlinear Autoregressive Exogenous (NARX)
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en11030620

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.