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Benchmarking of solar irradiance nowcast performance derived from all-sky imagers

Author

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  • Logothetis, Stavros-Andreas
  • Salamalikis, Vasileios
  • Wilbert, Stefan
  • Remund, Jan
  • Zarzalejo, Luis F.
  • Xie, Yu
  • Nouri, Bijan
  • Ntavelis, Evangelos
  • Nou, Julien
  • Hendrikx, Niels
  • Visser, Lennard
  • Sengupta, Manajit
  • Pó, Mário
  • Chauvin, Remi
  • Grieu, Stephane
  • Blum, Niklas
  • van Sark, Wilfried
  • Kazantzidis, Andreas

Abstract

Fluctuations of the incoming solar irradiance impact the power generation from photovoltaic and concentrating solar thermal power plants. Accurate solar nowcasting becomes necessary to detect these sudden changes of generated power and to provide the desired information for optimal exploitation of solar systems. In the framework of the International Energy Agency's Photovoltaic Power Systems Program Task 16, a benchmarking exercise has been conducted relying on a bouquet of solar nowcasting methodologies by all-sky imagers (ASI). In this paper, four ASI systems nowcast the Global Horizontal Irradiance (GHI) with a time forecast ranging from 1 to 20 min during 28 days with variable cloud conditions spanning from September to November 2019 in southern Spain. All ASIs demonstrated their ability to accurately nowcast GHI, with RMSE ranging from 6.9% to 18.1%. Under cloudy conditions, all ASIs' nowcasts outperform the persistence models. Under clear skies, three ASIs are better than persistence. Discrepancies in the observed nowcasting performance become larger at increasing forecast horizons. The findings of this study highlight the feasibility of ASIs to reliably nowcast GHI at different sky conditions, time intervals and horizons. Such nowcasts can be used either to estimate solar power at distant times or detect sudden GHI fluctuations.

Suggested Citation

  • Logothetis, Stavros-Andreas & Salamalikis, Vasileios & Wilbert, Stefan & Remund, Jan & Zarzalejo, Luis F. & Xie, Yu & Nouri, Bijan & Ntavelis, Evangelos & Nou, Julien & Hendrikx, Niels & Visser, Lenna, 2022. "Benchmarking of solar irradiance nowcast performance derived from all-sky imagers," Renewable Energy, Elsevier, vol. 199(C), pages 246-261.
    • Handle: RePEc:eee:renene:v:199:y:2022:i:c:p:246-261
    DOI: 10.1016/j.renene.2022.08.127
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    Cited by:

    1. Pan Xia & Lu Zhang & Min Min & Jun Li & Yun Wang & Yu Yu & Shengjie Jia, 2024. "Accurate nowcasting of cloud cover at solar photovoltaic plants using geostationary satellite images," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    2. Mathieu David & Joaquín Alonso-Montesinos & Josselin Le Gal La Salle & Philippe Lauret, 2023. "Probabilistic Solar Forecasts as a Binary Event Using a Sky Camera," Energies, MDPI, vol. 16(20), pages 1-18, October.
    3. Sergiu-Mihai Hategan & Nicoleta Stefu & Marius Paulescu, 2023. "An Ensemble Approach for Intra-Hour Forecasting of Solar Resource," Energies, MDPI, vol. 16(18), pages 1-12, September.
    4. Ogliari, Emanuele & Sakwa, Maciej & Cusa, Paolo, 2024. "Enhanced Convolutional Neural Network for solar radiation nowcasting: All-Sky camera infrared images embedded with exogeneous parameters," Renewable Energy, Elsevier, vol. 221(C).

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