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A study on the minimum duration of training data to provide a high accuracy forecast for PV generation between two different climatic zones

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  • Do, Minh-Thang
  • Soubdhan, Ted
  • Benoît Robyns,

Abstract

This study focus on the minimum duration of training data required for PV generation forecast. In order to investigate this issue, the study is implemented on 2 PV installations: the first one in Guadeloupe represented for tropical climate, the second in Lille represented for temperate climate; using 3 different forecast models: the Scaled Persistence Model, the Artificial Neural Network and the Multivariate Polynomial Model. The usual statistical forecasting error indicators: NMBE, NMAE and NRMSE are computed in order to compare the accuracy of forecasts.

Suggested Citation

  • Do, Minh-Thang & Soubdhan, Ted & Benoît Robyns,, 2016. "A study on the minimum duration of training data to provide a high accuracy forecast for PV generation between two different climatic zones," Renewable Energy, Elsevier, vol. 85(C), pages 959-964.
    • Handle: RePEc:eee:renene:v:85:y:2016:i:c:p:959-964
    DOI: 10.1016/j.renene.2015.07.057
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    References listed on IDEAS

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    1. Paulescu, Marius & Badescu, Viorel & Dughir, Ciprian, 2014. "New procedure and field-tests to assess photovoltaic module performance," Energy, Elsevier, vol. 70(C), pages 49-57.
    2. Fernandez-Jimenez, L. Alfredo & Muñoz-Jimenez, Andrés & Falces, Alberto & Mendoza-Villena, Montserrat & Garcia-Garrido, Eduardo & Lara-Santillan, Pedro M. & Zorzano-Alba, Enrique & Zorzano-Santamaria,, 2012. "Short-term power forecasting system for photovoltaic plants," Renewable Energy, Elsevier, vol. 44(C), pages 311-317.
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    Cited by:

    1. Hassan, Muhammed A. & Bailek, Nadjem & Bouchouicha, Kada & Nwokolo, Samuel Chukwujindu, 2021. "Ultra-short-term exogenous forecasting of photovoltaic power production using genetically optimized non-linear auto-regressive recurrent neural networks," Renewable Energy, Elsevier, vol. 171(C), pages 191-209.
    2. Ferlito, S. & Adinolfi, G. & Graditi, G., 2017. "Comparative analysis of data-driven methods online and offline trained to the forecasting of grid-connected photovoltaic plant production," Applied Energy, Elsevier, vol. 205(C), pages 116-129.
    3. Samu, Remember & Calais, Martina & Shafiullah, G.M. & Moghbel, Moayed & Shoeb, Md Asaduzzaman & Nouri, Bijan & Blum, Niklas, 2021. "Applications for solar irradiance nowcasting in the control of microgrids: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 147(C).

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