Short term cloud nowcasting for a solar power plant based on irradiance historical data

  • Rafael Caballero University Complutense of Madrid
  • Luis F. Zarzalejo Renewable Energy Division. Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT), 28040 Madrid, Spain
  • Álvaro Otero University Complutense de Madrid, 28040 Madrid, Spain
  • Luis Piñuel University Complutense de Madrid, 28040 Madrid, Spain
  • Stefan Wilbert Institute of Solar Research, German Aerospace Center (DLR), 04200 Tabernas, Spain.
Keywords: cloud nowcasting, GHI, LSTM, supervised machine learning

Abstract

This work considers the problem of forecasting the normal solar irradiance with high spatial and temporal resolution (5 minutes). The forecasting is based on a dataset registered during one year from the high resolution radiometric network at a operational solar power plan at Almeria, Spain. In particular, we show a technique for forecasting the irradiance in the next few minutes from the irradiance values obtained on the previous hour.  Our proposal employs a type of recurrent neural network known as LSTM, which can learn complex patterns and that has proven its usability for forecasting temporal series. The results show a reasonable improvement with respect to other prediction methods typically employed in the studies of temporal series.

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Published
2018-12-12
How to Cite
Caballero, R., Zarzalejo, L. F., Otero, Álvaro, Piñuel, L., & Wilbert, S. (2018). Short term cloud nowcasting for a solar power plant based on irradiance historical data. Journal of Computer Science and Technology, 18(03), e21. https://doi.org/10.24215/16666038.18.e21
Section
Original Articles