A Bayesian approach for site-specific wind rose prediction

Antonio Lepore, Biagio Palumbo and Antonio Pievatolo

Renewable Energy, 2020, vol. 150, issue C, 691-702

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.

Keywords: Wind rose; Site-specific prediction; Short-term data; Bayesian approach; Wind speed and direction joint distribution (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:150:y:2020:i:c:p:691-702

DOI: 10.1016/j.renene.2019.12.137

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