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Solar Irradiance Ramp Forecasting Based on All-Sky Imagers

Author

Listed:
  • Stavros-Andreas Logothetis

    (Laboratory of Atmospheric Physics, Physics Department, University of Patras, 26500 Patras, Greece)

  • Vasileios Salamalikis

    (Laboratory of Atmospheric Physics, Physics Department, University of Patras, 26500 Patras, Greece)

  • Bijan Nouri

    (German Aerospace Center (DLR), Institute of Solar Research, Paseo de Almería 73, 04001 Almería, Spain)

  • Jan Remund

    (Meteotest, 3012 Bern, Switzerland)

  • Luis F. Zarzalejo

    (CIEMAT Energy Department–Renewable Energy Division, Av. Complutense 40, 28040 Madrid, Spain)

  • Yu Xie

    (National Renewable Energy Laboratory, 1617 Cole Blvd, Golden, CO 80401, USA)

  • Stefan Wilbert

    (German Aerospace Center (DLR), Institute of Solar Research, Paseo de Almería 73, 04001 Almería, Spain)

  • Evangelos Ntavelis

    (CSEM Center Alpnach, 6055 Alpnach Dorf, Switzerland)

  • Julien Nou

    (PROMES-CNRS, Rambla de la Thermodynamique, 66100 Perpignan, France)

  • Niels Hendrikx

    (Copernicus Institute of Sustainable Development, Utrecht University, Princetonlaan 8, 3584 CB Utrecht, The Netherlands)

  • Lennard Visser

    (Copernicus Institute of Sustainable Development, Utrecht University, Princetonlaan 8, 3584 CB Utrecht, The Netherlands)

  • Manajit Sengupta

    (National Renewable Energy Laboratory, 1617 Cole Blvd, Golden, CO 80401, USA)

  • Mário Pó

    (EKO INSTRUMENTS Europe B.V., 2521 AL Den Haag, The Netherlands)

  • Remi Chauvin

    (PROMECA Ingénierie, 1 rue des Iles, 38420 Domène, France)

  • Stephane Grieu

    (PROMES Laboratory of Processes, Materials and Solar Energy, Rambla de la Thermodynamique, Université de Perpignan, 66100 Perpignan, France)

  • Niklas Blum

    (German Aerospace Center (DLR), Institute of Solar Research, Paseo de Almería 73, 04001 Almería, Spain)

  • Wilfried van Sark

    (Copernicus Institute of Sustainable Development, Utrecht University, Princetonlaan 8, 3584 CB Utrecht, The Netherlands)

  • Andreas Kazantzidis

    (Laboratory of Atmospheric Physics, Physics Department, University of Patras, 26500 Patras, Greece)

Abstract

Solar forecasting constitutes a critical tool for operating, producing and storing generated power from solar farms. In the framework of the International Energy Agency’s Photovoltaic Power Systems Program Task 16, the solar irradiance nowcast algorithms, based on five all-sky imagers (ASIs), are used to investigate the feasibility of ASIs to foresee ramp events. ASIs 1–2 and ASIs 3–5 can capture the true ramp events by 26.0–51.0% and 49.0–92.0% of the cases, respectively. ASIs 1–2 provided the lowest (<10.0%) falsely documented ramp events while ASIs 3–5 recorded false ramp events up to 85.0%. On the other hand, ASIs 3–5 revealed the lowest falsely documented no ramp events (8.0–51.0%). ASIs 1–2 are developed to provide spatial solar irradiance forecasts and have been delimited only to a small area for the purposes of this benchmark, which penalizes these approaches. These findings show that ASI-based nowcasts could be considered as a valuable tool for predicting solar irradiance ramp events for a variety of solar energy technologies. The combination of physical and deep learning-based methods is identified as a potential approach to further improve the ramp event forecasts.

Suggested Citation

  • Stavros-Andreas Logothetis & Vasileios Salamalikis & Bijan Nouri & Jan Remund & Luis F. Zarzalejo & Yu Xie & Stefan Wilbert & Evangelos Ntavelis & Julien Nou & Niels Hendrikx & Lennard Visser & Manaji, 2022. "Solar Irradiance Ramp Forecasting Based on All-Sky Imagers," Energies, MDPI, vol. 15(17), pages 1-17, August.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:17:p:6191-:d:897923
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    References listed on IDEAS

    as
    1. Abuella, Mohamed & Chowdhury, Badrul, 2019. "Forecasting of solar power ramp events: A post-processing approach," Renewable Energy, Elsevier, vol. 133(C), pages 1380-1392.
    2. Reno, Matthew J. & Hansen, Clifford W., 2016. "Identification of periods of clear sky irradiance in time series of GHI measurements," Renewable Energy, Elsevier, vol. 90(C), pages 520-531.
    3. Kong, Weicong & Jia, Youwei & Dong, Zhao Yang & Meng, Ke & Chai, Songjian, 2020. "Hybrid approaches based on deep whole-sky-image learning to photovoltaic generation forecasting," Applied Energy, Elsevier, vol. 280(C).
    4. Caldas, M. & Alonso-Suárez, R., 2019. "Very short-term solar irradiance forecast using all-sky imaging and real-time irradiance measurements," Renewable Energy, Elsevier, vol. 143(C), pages 1643-1658.
    5. Cui, Mingjian & Zhang, Jie & Feng, Cong & Florita, Anthony R. & Sun, Yuanzhang & Hodge, Bri-Mathias, 2017. "Characterizing and analyzing ramping events in wind power, solar power, load, and netload," Renewable Energy, Elsevier, vol. 111(C), pages 227-244.
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    Cited by:

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    5. Mohammed Asloune & Gilles Notton & Cyril Voyant, 2025. "From Trends to Insights: A Text Mining Analysis of Solar Energy Forecasting (2017–2023)," Energies, MDPI, vol. 18(19), pages 1-30, October.

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