Solar Radiation Prediction Using a Novel Hybrid Model of ARMA and NARX

Ines Sansa, Zina Boussaada and Najiba Mrabet Bellaaj
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Ines Sansa: Laboratory of Electrical Systems, National Engineers School of Tunis, University of Tunis El Manar, LR11ES15, Tunis 1002, Tunisia
Zina Boussaada: ESTIA Institute of Technology, University Bordeaux, F-64210 Bidart, France
Najiba Mrabet Bellaaj: Laboratory of Electrical Systems, National Engineers School of Tunis, University of Tunis El Manar, LR11ES15, Tunis 1002, Tunisia

Energies, 2021, vol. 14, issue 21, 1-26

Abstract: The prediction of solar radiation has a significant role in several fields such as photovoltaic (PV) power production and micro grid management. The interest in solar radiation prediction is increasing nowadays so efficient prediction can greatly improve the performance of these different applications. This paper presents a novel solar radiation prediction approach which combines two models, the Auto Regressive Moving Average (ARMA) and the Nonlinear Auto Regressive with eXogenous input (NARX). This choice has been carried out in order to take the advantages of both models to produce better prediction results. The performance of the proposed hybrid model has been validated using a real database corresponding to a company located in Barcelona north. Simulation results have proven the effectiveness of this hybrid model to predict the weekly solar radiation averages. The ARMA model is suitable for small variations of solar radiation while the NARX model is appropriate for large solar radiation fluctuations.

Keywords: solar radiation; PV power; prediction; ARMA; NARX; hybrid model (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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