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KloudNet: Deep Learning for Sky Image Analysis and Irradiance Forecasting

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Pattern Recognition (GCPR 2018)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11269))

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Abstract

We present a novel image-based approach for estimating irradiance fluctuations from sky images. Our goal is a very short-term prediction of the irradiance state around a photovoltaic power plant 5–10 min ahead of time, in order to adjust alternative energy sources and ensure a stable energy network. To this end, we propose a convolutional neural network with residual building blocks that learns to predict the future irradiance state from a small set of sky images. Our experiments on two large datasets demonstrate that the network abstracts upon local site-specific properties such as day- and month-dependent sun positions, as well as generic properties about moving, creating, dissolving clouds, or seasonal changes. Moreover, our approach significantly outperforms the established baseline and state-of-the-art methods.

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Acknowledgements

This work received funding from the Horizon 2020 research and innovation programme under grant No. 637221 (Built2Spec).

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Authors and Affiliations

  1. ETH Zürich, Zürich, Switzerland

    Dinesh Pothineni, Martin R. Oswald & Marc Pollefeys

  2. ABB Corporate Research, Baden, Switzerland

    Jan Poland

Authors
  1. Dinesh Pothineni
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  2. Martin R. Oswald
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  3. Jan Poland
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  4. Marc Pollefeys
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Corresponding author

Correspondence to Martin R. Oswald .

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Editors and Affiliations

  1. University of Freiburg, Freiburg im Breisgau, Baden-Württemberg, Germany

    Thomas Brox

  2. University of Stuttgart, Stuttgart, Baden-Württemberg, Germany

    Andrés Bruhn

  3. CISPA Helmholtz Center for Information Security, Saarbrücken, Germany

    Mario Fritz

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Pothineni, D., Oswald, M.R., Poland, J., Pollefeys, M. (2019). KloudNet: Deep Learning for Sky Image Analysis and Irradiance Forecasting. In: Brox, T., Bruhn, A., Fritz, M. (eds) Pattern Recognition. GCPR 2018. Lecture Notes in Computer Science(), vol 11269. Springer, Cham. https://doi.org/10.1007/978-3-030-12939-2_37

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  • DOI: https://doi.org/10.1007/978-3-030-12939-2_37

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-12938-5

  • Online ISBN: 978-3-030-12939-2

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