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Modelling the variability of the wind energy resource on monthly and seasonal timescales

Author

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  • Alonzo, Bastien
  • Ringkjob, Hans-Kristian
  • Jourdier, Benedicte
  • Drobinski, Philippe
  • Plougonven, Riwal
  • Tankov, Peter

Abstract

An avenue for modelling part of the long-term variability of the wind energy resource from knowledge of the large-scale state of the atmosphere is investigated. The timescales considered are monthly to seasonal, and the focus is on France and its vicinity. On such timescales, one may obtain information on likely surface winds from the large-scale state of the atmosphere, determining for instance the most likely paths for storms impinging on Europe. In a first part, we reconstruct surface wind distributions on monthly and seasonal timescales from the knowledge of the large-scale state of the atmosphere, which is summarized using a principal components analysis. We then apply a multi-polynomial regression to model surface wind speed distributions in the parametric context of the Weibull distribution. Several methods are tested for the reconstruction of the parameters of the Weibull distribution, and some of them show good performance. This proves that there is a significant potential for information in the relation between the synoptic circulation and the surface wind speed. In the second part of the paper, the knowledge obtained on the relationship between the large-scale situation of the atmosphere and surface wind speeds is used in an attempt to forecast wind speeds distributions on a monthly horizon. The forecast results are promising but they also indicate that the Numerical Weather Prediction seasonal forecasts on which they are based, are not yet mature enough to provide reliable information for timescales exceeding one month.

Suggested Citation

  • Alonzo, Bastien & Ringkjob, Hans-Kristian & Jourdier, Benedicte & Drobinski, Philippe & Plougonven, Riwal & Tankov, Peter, 2017. "Modelling the variability of the wind energy resource on monthly and seasonal timescales," Renewable Energy, Elsevier, vol. 113(C), pages 1434-1446.
    • Handle: RePEc:eee:renene:v:113:y:2017:i:c:p:1434-1446
    DOI: 10.1016/j.renene.2017.07.019
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    References listed on IDEAS

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    1. Guo, Zhenhai & Zhao, Weigang & Lu, Haiyan & Wang, Jianzhou, 2012. "Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model," Renewable Energy, Elsevier, vol. 37(1), pages 241-249.
    2. Sailor, David J. & Smith, Michael & Hart, Melissa, 2008. "Climate change implications for wind power resources in the Northwest United States," Renewable Energy, Elsevier, vol. 33(11), pages 2393-2406.
    3. Christophe Cassou, 2008. "Intraseasonal interaction between the Madden–Julian Oscillation and the North Atlantic Oscillation," Nature, Nature, vol. 455(7212), pages 523-527, September.
    4. Wang, Jianzhou & Qin, Shanshan & Zhou, Qingping & Jiang, Haiyan, 2015. "Medium-term wind speeds forecasting utilizing hybrid models for three different sites in Xinjiang, China," Renewable Energy, Elsevier, vol. 76(C), pages 91-101.
    5. Lun, Isaac Y.F & Lam, Joseph C, 2000. "A study of Weibull parameters using long-term wind observations," Renewable Energy, Elsevier, vol. 20(2), pages 145-153.
    6. Pryor, S.C. & Barthelmie, R.J., 2010. "Climate change impacts on wind energy: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(1), pages 430-437, January.
    7. Sfetsos, A., 2002. "A novel approach for the forecasting of mean hourly wind speed time series," Renewable Energy, Elsevier, vol. 27(2), pages 163-174.
    8. Bilgili, Mehmet & Sahin, Besir & Yasar, Abdulkadir, 2007. "Application of artificial neural networks for the wind speed prediction of target station using reference stations data," Renewable Energy, Elsevier, vol. 32(14), pages 2350-2360.
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    Cited by:

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    5. Alonzo, Bastien & Tankov, Peter & Drobinski, Philippe & Plougonven, Riwal, 2020. "Probabilistic wind forecasting up to three months ahead using ensemble predictions for geopotential height," International Journal of Forecasting, Elsevier, vol. 36(2), pages 515-530.

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