A Bayesian approach to boost wind parameter estimation by fusing historical and sampling data

In the last decade wind energy has proven to be one of the most competitive and fastest growing sources of renewable energy. Currently the problem of evaluating the site-specific wind potential is showing the main difficulties since it is faced only using on-site anemometric long-term monitoring and wind atlases. Usually information published on wind atlases traces a territorial map of the wind speeds in correspondence with different heights. This documents represent a good starting-point for a preliminary site analysis, but it fails to give reliable customized information to support the evaluation and, eventually, the activation of potential investments. Undoubtedly, the on-site direct anemometric monitoring gives more reliable information because it is directly taken from the site. However, it is costly and lengthy because wide temporal windows (of at least one year) are required to accurately characterize the site-specific wind potential. In order to obtain a both reliable and timely analysis, this paper proposes to integrate the above two sources of knowledge by a Bayesian approach, implemented via Markov chain Monte Carlo (MCMC). The proposed methodology, combining prior information (e.g. obtained from atlases and/or fluid-dynamic assessment) with sampling data, furnishes robust and timely posterior information. Real sampling data, collected from a southern Italian site, are analysed in order to illustrate the main features of the proposed methodology and to test the adopted filtering strategy to face the high correlation which characterize the anemometric data. The results of the example show that the proposed Bayes methodology fits the applicative needs very well. In particular the attained precision of the estimates carried out from one-month sample is comparable to the one of the ML estimates from the corresponding whole (one-year) sample