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Densidad de predicción basada en momentos condicionados y máxima entropía : aplicación a la predicción de potencia eólica

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Author Info

  • Miguel Ángel Bermejo

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  • Daniel Peña

    ()

  • Ismael Sánchez

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    Abstract

    El cálculo de predicciones puntuales junto con su incertidumbre en forma de intervalo es, en la mayoría de aplicaciones, insuficiente. Especialmente cuando estemos asumiendo no linealidad en los datos, puesto que en estos casos, podrían existir incluso cambios en la distribución. Por ello será necesario, además de la predicción puntual, obtener una estimación de la densidad condicionada de la variable en el futuro dado su comportamiento actual, es decir, la densidad predictiva. En este trabajo proponemos una estimación de la densidad predictiva empleando diferentes distribuciones paramétricas como son la Normal Truncada, la Normal Censurada, la Beta y la de Máxima Entropía. Dichas distribuciones serán calculadas empleando los momentos condicionados estimados mediante un método de estimación recursiva. Se aplica el procedimiento a datos provenientes de energía eólica

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    Bibliographic Info

    Paper provided by Universidad Carlos III, Departamento de Estadística y Econometría in its series Statistics and Econometrics Working Papers with number ws111813.

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    Date of creation: Jun 2011
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    Handle: RePEc:cte:wsrepe:ws111813

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    Keywords: Densidad predictiva; Máxima entropía; Momentos condicionados; Estimación recursiva; Potencia eólica;

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    1. González-Rivera, Gloria & Senyuz, Zeynep & Yoldas, Emre, 2011. "Autocontours: Dynamic Specification Testing," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(1), pages 186-200.
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    8. Anthony Tay & Kenneth F. Wallis, 2000. "Density Forecasting: A Survey," Econometric Society World Congress 2000 Contributed Papers 0370, Econometric Society.
    9. 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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