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

Author

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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
    Higher Institute of Computing, University of Tunis El Manar, Ariana 2080, Tunisia)

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.

Suggested Citation

  • Ines Sansa & Zina Boussaada & Najiba Mrabet Bellaaj, 2021. "Solar Radiation Prediction Using a Novel Hybrid Model of ARMA and NARX," Energies, MDPI, vol. 14(21), pages 1-26, October.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:21:p:6920-:d:661561
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    References listed on IDEAS

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    Cited by:

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    2. Haobo Shi & Yanping Xu & Baodi Ding & Jinsong Zhou & Pei Zhang, 2023. "Long-Term Solar Power Time-Series Data Generation Method Based on Generative Adversarial Networks and Sunrise–Sunset Time Correction," Sustainability, MDPI, vol. 15(20), pages 1-19, October.
    3. Ze Wu & Feifan Pan & Dandan Li & Hao He & Tiancheng Zhang & Shuyun Yang, 2022. "Prediction of Photovoltaic Power by the Informer Model Based on Convolutional Neural Network," Sustainability, MDPI, vol. 14(20), pages 1-16, October.

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