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Sun Position Identification in Sky Images for Nowcasting Application

Author

Listed:
  • Alessandro Niccolai

    (Politecnico di Milano, Dipartimento di Energia, Via La Masa, 34, 20156 Milan, Italy)

  • Alfredo Nespoli

    (Politecnico di Milano, Dipartimento di Energia, Via La Masa, 34, 20156 Milan, Italy)

Abstract

Very-short-term photovoltaic power forecast, namely nowcasting, is gaining increasing attention to face grid stability issues and to optimize microgrid energy management systems in the presence of large penetration of renewable energy sources. In order to identify local phenomena as sharp ramps in photovoltaic production, whole sky images can be used effectively. The first step in the implementation of new and effective nowcasting algorithms is the identification of Sun positions. In this paper, three different techniques (solar angle-based, image processing-based, and neural network-based techniques) are proposed, described, and compared. These techniques are tested on real images obtained with a camera installed at SolarTech Lab at Politecnico di Milano, Milan, Italy. Finally, the three techniques are compared by introducing some performance parameters aiming to evaluate of their reliability, accuracy, and computational effort. The neural network-based technique obtains the best performance: in fact, this method is able to identify accurately the Sun position and to estimate it when the Sun is covered by clouds.

Suggested Citation

  • Alessandro Niccolai & Alfredo Nespoli, 2020. "Sun Position Identification in Sky Images for Nowcasting Application," Forecasting, MDPI, vol. 2(4), pages 1-17, November.
    • Handle: RePEc:gam:jforec:v:2:y:2020:i:4:p:26-504:d:445576
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. Alessandro Niccolai & Alberto Dolara & Emanuele Ogliari, 2021. "Hybrid PV Power Forecasting Methods: A Comparison of Different Approaches," Energies, MDPI, vol. 14(2), pages 1-18, January.
    3. Alessandro Niccolai & Emanuele Ogliari & Alfredo Nespoli & Riccardo Zich & Valentina Vanetti, 2022. "Very Short-Term Forecast: Different Classification Methods of the Whole Sky Camera Images for Sudden PV Power Variations Detection," Energies, MDPI, vol. 15(24), pages 1-16, December.
    4. Nespoli, Alfredo & Niccolai, Alessandro & Ogliari, Emanuele & Perego, Giovanni & Collino, Elena & Ronzio, Dario, 2022. "Machine Learning techniques for solar irradiation nowcasting: Cloud type classification forecast through satellite data and imagery," Applied Energy, Elsevier, vol. 305(C).

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