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Irradiance Nowcasting by Means of Deep-Learning Analysis of Infrared Images

Author

Listed:
  • Alessandro Niccolai

    (Department of Energy, Politecnico di Milano, Via La Masa 34, 20156 Milan, Italy)

  • Seyedamir Orooji

    (Department of Energy, Politecnico di Milano, Via La Masa 34, 20156 Milan, Italy)

  • Andrea Matteri

    (Department of Energy, Politecnico di Milano, Via La Masa 34, 20156 Milan, Italy)

  • Emanuele Ogliari

    (Department of Energy, Politecnico di Milano, Via La Masa 34, 20156 Milan, Italy)

  • Sonia Leva

    (Department of Energy, Politecnico di Milano, Via La Masa 34, 20156 Milan, Italy)

Abstract

This work proposes and evaluates a method for the nowcasting of solar irradiance variability in multiple time horizons, namely 5, 10, and 15 min ahead. The method is based on a Convolutional Neural Network structure that exploits infrared sky images acquired through an All-Sky Imager to estimate the range of possible values that the Clear-Sky Index will possibly assume over a selected forecast horizon. All data available, from the infrared images to the measurements of Global Horizontal Irradiance (necessary in order to compute Clear-Sky Index), are acquired at SolarTech LAB in Politecnico di Milano. The proposed method demonstrated a discrete performance level, with an accuracy peak for the 5 min time horizon, where about 65% of the available samples are attributed to the correct range of Clear-Sky Index values.

Suggested Citation

  • Alessandro Niccolai & Seyedamir Orooji & Andrea Matteri & Emanuele Ogliari & Sonia Leva, 2022. "Irradiance Nowcasting by Means of Deep-Learning Analysis of Infrared Images," Forecasting, MDPI, vol. 4(1), pages 1-11, March.
    • Handle: RePEc:gam:jforec:v:4:y:2022:i:1:p:19-348:d:763971
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    References listed on IDEAS

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