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A new probabilistic method to estimate the long-term wind speed characteristics at a potential wind energy conversion site

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  • Carta, José A.
  • Velázquez, Sergio

Abstract

This paper proposes the use of a new Measure–Correlate–Predict (MCP) method to estimate the long-term wind speed characteristics at a potential wind energy conversion site. The proposed method uses the probability density function of the wind speed at a candidate site conditioned to the wind speed at a reference site. Contingency-type bivariate distributions with specified marginal distributions are used for this purpose. The proposed model was applied in this paper to wind speeds recorded at six weather stations located in the Canary Islands (Spain). The conclusion reached is that the method presented in this paper, in the majority of cases, provides better results than those obtained with other MCP methods used for purposes of comparison. The metrics employed in the analysis were the coefficient of determination (R2) and the root relative squared error (RRSE). The characteristics that were analysed were the capacity of the model to estimate the long-term wind speed probability distribution function, the long-term wind power density probability distribution function and the long-term wind turbine power output probability distribution function at the candidate site.

Suggested Citation

  • Carta, José A. & Velázquez, Sergio, 2011. "A new probabilistic method to estimate the long-term wind speed characteristics at a potential wind energy conversion site," Energy, Elsevier, vol. 36(5), pages 2671-2685.
    • Handle: RePEc:eee:energy:v:36:y:2011:i:5:p:2671-2685
    DOI: 10.1016/j.energy.2011.02.008
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    References listed on IDEAS

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    1. Velázquez, Sergio & Carta, José A. & Matías, J.M., 2011. "Influence of the input layer signals of ANNs on wind power estimation for a target site: A case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(3), pages 1556-1566, April.
    2. Carta, J.A. & Ramírez, P., 2007. "Analysis of two-component mixture Weibull statistics for estimation of wind speed distributions," Renewable Energy, Elsevier, vol. 32(3), pages 518-531.
    3. Breslow, Paul B. & Sailor, David J., 2002. "Vulnerability of wind power resources to climate change in the continental United States," Renewable Energy, Elsevier, vol. 27(4), pages 585-598.
    4. Carta, J.A. & Ramírez, P. & Velázquez, S., 2009. "A review of wind speed probability distributions used in wind energy analysis: Case studies in the Canary Islands," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(5), pages 933-955, June.
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