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A Review on Physical and Data-Driven Based Nowcasting Methods Using Sky Images

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Advances in Information and Communication (FICC 2021)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1364))

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Abstract

Amongst all the renewable energy resources (RES), solar is the most popular form of energy source and is of particular interest for its widely integration into the power grid. However, due to the intermittent nature of solar source, it is of the greatest significance to forecast solar irradiance to ensure uninterrupted and reliable power supply to serve the energy demand. There are several approaches to perform solar irradiance forecasting, for instance satellite-based methods, sky image-based methods, machine learning-based methods, and numerical weather prediction-based methods. In this paper, we present a review on short-term intra-hour solar prediction techniques known as nowcasting methods using sky images. Along with this, we also report and discuss which sky image features are significant for the nowcasting methods.

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Notes

  1. 1.

    https://maps.nrel.gov/nsrdb-viewer/.

  2. 2.

    https://midcdmz.nrel.gov/apps/sitehome.pl?site=OAHUGRID.

  3. 3.

    https://www.esrl.noaa.gov/gmd/grad/surfrad/dataplot.html.

  4. 4.

    http://www.soda-pro.com/web-services.

  5. 5.

    https://dds.cr.usgs.gov/srtm/.

  6. 6.

    https://zenodo.org/record/2826939##.X5_5uUJKhTY.

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Authors and Affiliations

  1. Institute of Networked and Embedded Systems, Lakeside Labs Alpen-Adria-Universität, Klagenfurt, Austria

    Ekanki Sharma & Wilfried Elmenreich

Authors
  1. Ekanki Sharma
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  2. Wilfried Elmenreich
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Correspondence to Ekanki Sharma .

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Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

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Sharma, E., Elmenreich, W. (2021). A Review on Physical and Data-Driven Based Nowcasting Methods Using Sky Images. In: Arai, K. (eds) Advances in Information and Communication. FICC 2021. Advances in Intelligent Systems and Computing, vol 1364. Springer, Cham. https://doi.org/10.1007/978-3-030-73103-8_24

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