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Solar irradiation nowcasting by stochastic persistence: A new parsimonious, simple and efficient forecasting tool

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  • Voyant, Cyril
  • Notton, Gilles

Abstract

Simple, naïve, smart or clearness persistences are tools largely used as naïve predictors for the global solar irradiation forecasting. It is essential to compare the performances of sophisticated prediction approaches with that of a reference approach generally a naïve methods. In this paper, a new kind of naïve “nowcaster” is developed, a persistence model based on the stochastic aspect of measured solar energy signal denoted stochastic persistence and constructed without needing a large collection of historical data. Two versions are proposed: one based on an additive and one on a multiplicative scheme; a theoretical description and an experimental validation based on measurements realized in Ajaccio (France) and Tilos (Greece) are exposed. The results show that this approach is efficient, easy to implement and does not need historical data as the machine learning methods usually employed. This new solar irradiation predictor could become an interesting tool and become a new member of the solar forecasting family.

Suggested Citation

  • Voyant, Cyril & Notton, Gilles, 2018. "Solar irradiation nowcasting by stochastic persistence: A new parsimonious, simple and efficient forecasting tool," Renewable and Sustainable Energy Reviews, Elsevier, vol. 92(C), pages 343-352.
    • Handle: RePEc:eee:rensus:v:92:y:2018:i:c:p:343-352
    DOI: 10.1016/j.rser.2018.04.116
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    Citations

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    Cited by:

    1. Marchesoni-Acland, Franco & Alonso-Suárez, Rodrigo, 2020. "Intra-day solar irradiation forecast using RLS filters and satellite images," Renewable Energy, Elsevier, vol. 161(C), pages 1140-1154.
    2. Feng, Cong & Zhang, Jie & Zhang, Wenqi & Hodge, Bri-Mathias, 2022. "Convolutional neural networks for intra-hour solar forecasting based on sky image sequences," Applied Energy, Elsevier, vol. 310(C).
    3. Lopes, Francis M. & Conceição, Ricardo & Fasquelle, Thomas & Silva, Hugo G. & Salgado, Rui & Canhoto, Paulo & Collares-Pereira, Manuel, 2020. "Predicted direct solar radiation (ECMWF) for optimized operational strategies of linear focus parabolic-trough systems," Renewable Energy, Elsevier, vol. 151(C), pages 378-391.
    4. Rodríguez, Fermín & Martín, Fernando & Fontán, Luis & Galarza, Ainhoa, 2021. "Ensemble of machine learning and spatiotemporal parameters to forecast very short-term solar irradiation to compute photovoltaic generators’ output power," Energy, Elsevier, vol. 229(C).
    5. Francis M. Lopes & Ricardo Conceição & Hugo G. Silva & Thomas Fasquelle & Rui Salgado & Paulo Canhoto & Manuel Collares-Pereira, 2019. "Short-Term Forecasts of DNI from an Integrated Forecasting System (ECMWF) for Optimized Operational Strategies of a Central Receiver System," Energies, MDPI, vol. 12(7), pages 1-18, April.

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