A Bayesian approach for site-specific wind rose prediction. (May 2020) - Lepore, Antonio; Palumbo, Biagio; Pievatolo, Antonio
Abstract: Feasibility of wind-farm projects emphasizes the need for a timely evaluation of the site-specific wind potential (i.e., the electrical power production from wind sources) because, unfortunately, it is usually hampered by the need for long-term anemometric sampling. In contrast, short-term (i.e., less than one year's worth of) data may not contain enough information if collected when the wind is not blowing from the prevailing direction(s). From a technological point of view, wind turbine performances drops down when they work one in trail of another, then wind direction plays a strategic role since it determines the optimal wind-farm layout design. By means of a real-case study, a Bayesian approach is proposed and shown to be capable of enhancing the annual wind rose prediction at the given candidate site by integrating the short-term sample data with both historical information (from a neighboring survey station) and expert opinion. Predictive wind direction and speed distributions are obtained marginally at first. Then, the predictive wind rose is derived by non-parametric modeling of the dependency between wind speed and direction based on the short-term data collected at the candidate site. Highlights: Wind rose estimation with short-term sample of wind at the candidate site. Incorporation of information from neighbouring sites via the Bayesian approach. Recovery of joint distribution of wind direction and speed via an empirical copula.