Solar Irradiance Ramp Forecasting Based on All-Sky Imagers

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

Research output: Contribution to journalArticlepeer-review

9 Scopus Citations

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.

Original languageAmerican English
Article number6191
Number of pages17
JournalEnergies
Volume15
Issue number17
DOIs
StatePublished - 2022

Bibliographical note

Publisher Copyright:
© 2022 by the authors.

NREL Publication Number

  • NREL/JA-5D00-84191

Keywords

  • all-sky imagers
  • forecasting
  • ramp events
  • solar irradiance ramp event forecasting

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