ENBIS-16 in Sheffield

11 – 15 September 2016; Sheffield Abstract submission: 20 March – 4 July 2016

A Bayesian Short-Term Strategy for Site-Specific Wind Parameter Estimation

13 September 2016, 16:20 – 16:40

Abstract

Submitted by
Antonio Lepore
Authors
Pasquale Erto (University of Naples Federico II), Antonio Lepore (University of Naples Federico II), Biagio Palumbo (University of Naples Federico II)
Abstract
The economic profitability of a candidate site is strongly influenced not only by the wind speed, which is needed to define the turbine type to be installed, but also by its direction, which represents a dominant parameter in the wind-farm layout design. Wind potential assessment from short (e.g., one-month) sample can be severely poor if performed when the wind is not blowing from the prevailing direction(s). This paper proposes a Bayesian approach in order to hasten directional data analysis collected from an Italian candidate site. In particular, the wind direction is grouped in 8 sectors and modelled with the multinomial distribution. The Dirichlet distribution is chosen as prior and is calibrated on the historical data available at a neighbouring survey station. In particular, the prior distribution elicitation is based on the Fisher’s angular-angular association between directional data with speed value greater than 4 m/s , which are collected simultaneously from the candidate site and the neighbouring survey station. In expert opinion, such threshold value represents the least speed that effectively activates turbines. Then, the Bayesian approach proposed in Erto et al (2010), which involves MCMC (Markov chain Monte Carlo) method, is opportunely adjusted in order to furnish the estimates of the wind speed distribution grouped by angular sector. In such a way, prior information on wind features can be better incorporated in a more familiar way into the adopted prior distributions for the Weibull model parameters. The attained results based on the Mean Square Error show that the rose-plot based on the Bayesian estimates carried out from a 1-month sample is comparable to the actual 1-year one. Such analysis is proposed to cope with actual problems faced by renewable energy companies as encouragingly shown by an application to real anemometric data from a Southern Italian site.

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