Wind speed parameter estimation from one‐month sample via Bayesian approach

Abstract

AbstractSite‐specific wind potential assessment shows difficulties mainly because it needs very long on‐site anemometric monitoring. This paper proposes to reduce the long‐term monitoring by exploiting other initial information about parameters to be estimated via MCMC (Markov chain Monte Carlo). The proposed Bayesian approach allows the integration of prior information (e.g. obtained from atlases, databases and/or fluid‐dynamic assessment) with sampling data, and furnishes effective and timely posterior information about Weibull parameters of the wind speed distribution. Real sampling data, collected from a southern Italian site, are analysed in order to illustrate the main features of the methodology. Moreover, the effectiveness of both the filtering strategy adopted to deal with the high correlation that usually characterizes the anemometric data, and the seasonal adjustment proposed to obtain a sample unbiased by seasonal effect is highlighted. The results of the application show that the proposed methodology fits the applicative needs very well. A bootstrap simulation remarks that the attained precision of the Bayesian estimates carried out from a one‐month sample is comparable to the maximum likelihood estimates obtained from an actual one‐year sample. Copyright © 2010 John Wiley & Sons, Ltd.