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Systematical Evaluation of Solar Energy Supply Forecasts

Part of the Lecture Notes in Computer Science book series (LNAI,volume 8817)

Abstract

The capacity of renewable energy sources constantly increases world-wide and challenges the maintenance of the electric balance between power demand and supply. To allow for a better integration of solar energy supply into the power grids, a lot of research was dedicated to the development of precise forecasting approaches. However, there is still no straightforward and easy-to-use recommendation for a standardized forecasting strategy. In this paper, a classification of solar forecasting solutions proposed in the literature is provided for both weather- and energy forecast models. Subsequently, we describe our idea of a standardized forecasting process and the typical parameters possibly influencing the selection of a specific model. We discuss model combination as an optimization option and evaluate this approach comparing different statistical algorithms against flexible hybrid models in a case study.

Keywords

  • Solar energy
  • Energy forecast model
  • Classification
  • Ensemble

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  • DOI: 10.1007/978-3-319-13290-7_9
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Notes

  1. 1.

    http://www.ecmwf.int

  2. 2.

    http://www.ncep.noaa.gov

  3. 3.

    http://www.ncdc.noaa.gov

  4. 4.

    http://www.mirabel-project.eu/

  5. 5.

    http://www.robotron.eu/

  6. 6.

    http://www.stevengould.org/software/openforecast/

  7. 7.

    http://www.en-apolda.de

  8. 8.

    http://www.50hertz.com

  9. 9.

    http://wetterstationen.meteomedia.de

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Acknowledgment

The work presented in this paper was funded by the European Regional Development Fund (EFRE) and the Free State of Saxony under the grant agreement number 100081313 and co-financed by Robotron Datenbank-Software GmbH. We thank Claudio Hartmann and Andreas Essbaumer for supporting our work.

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Correspondence to Robert Ulbricht .

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Ulbricht, R., Hahmann, M., Donker, H., Lehner, W. (2014). Systematical Evaluation of Solar Energy Supply Forecasts. In: Woon, W., Aung, Z., Madnick, S. (eds) Data Analytics for Renewable Energy Integration. DARE 2014. Lecture Notes in Computer Science(), vol 8817. Springer, Cham. https://doi.org/10.1007/978-3-319-13290-7_9

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  • DOI: https://doi.org/10.1007/978-3-319-13290-7_9

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