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Comparison of two new short-term wind-power forecasting systems

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  • Ramirez-Rosado, Ignacio J.
  • Fernandez-Jimenez, L. Alfredo
  • Monteiro, Cláudio
  • Sousa, João
  • Bessa, Ricardo

Abstract

This paper presents a comparison of two new advanced statistical short-term wind-power forecasting systems developed by two independent research teams. The input variables used in both systems were the same: forecasted meteorological variable values obtained from a numerical weather prediction model; and electric power-generation registers from the SCADA system of the wind farm. Both systems are described in detail and the forecasting results compared, revealing great similarities, although the proposed structures of the two systems are different. The forecast horizon for both systems is 72h, allowing the use of the forecasted values in electric market operations, as diary and intra-diary power generation bid offers, and in wind-farm maintenance planning.

Suggested Citation

  • Ramirez-Rosado, Ignacio J. & Fernandez-Jimenez, L. Alfredo & Monteiro, Cláudio & Sousa, João & Bessa, Ricardo, 2009. "Comparison of two new short-term wind-power forecasting systems," Renewable Energy, Elsevier, vol. 34(7), pages 1848-1854.
    • Handle: RePEc:eee:renene:v:34:y:2009:i:7:p:1848-1854
    DOI: 10.1016/j.renene.2008.11.014
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    References listed on IDEAS

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    1. Goh, S.L. & Chen, M. & Popović, D.H. & Aihara, K. & Obradovic, D. & Mandic, D.P., 2006. "Complex-valued forecasting of wind profile," Renewable Energy, Elsevier, vol. 31(11), pages 1733-1750.
    2. Roulston, M.S. & Kaplan, D.T. & Hardenberg, J. & Smith, L.A., 2003. "Using medium-range weather forcasts to improve the value of wind energy production," Renewable Energy, Elsevier, vol. 28(4), pages 585-602.
    3. 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.
    4. Peter Meibom & Torben Svendsen & Bent Sorensen, 1999. "Trading wind in a hydro-dominated power pool system," International Journal of Sustainable Development, Inderscience Enterprises Ltd, vol. 2(4), pages 458-483.
    5. Costa, Alexandre & Crespo, Antonio & Navarro, Jorge & Lizcano, Gil & Madsen, Henrik & Feitosa, Everaldo, 2008. "A review on the young history of the wind power short-term prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 12(6), pages 1725-1744, August.
    6. 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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