The Ornstein-Uhlenbeck process for estimating wind power under a memoryless transformation

J. Pablo Arenas-López and Mohamed Badaoui

Energy, 2020, vol. 213, issue C

Abstract: The main purpose is to build a wind speed model to assess its impact on the power output estimate of a wind turbine. This modelling is based on stochastic differential equations and memoryless transformations, considering a stable numerical scheme capable to optimize the computational complexity. According to statistical characteristics of a wind speed dataset recorded at a given location in Mexico, the model is parameterized for six probability density functions (PDF), showing exponential decay of the autocorrelation function. The suitability of the model for wind speed modelling and power output estimate is evaluated through a comparative analysis between the real dataset and the simulations; moreover, the quality of the findings have been assessed employing statistical measures criteria. Finally, and contrary to what has been reported, that is, the Weibull PDF is the best one to characterize wind speed in many regions of the world including Mexico, it turns out that among the six PDFs considered in this work, the probability density functions Beta and Log-Pearson 3 represent the best fit to the real wind speed data as well as providing an estimate of less than 1% of the semiannual produced energy by a wind turbine.

Keywords: Wind speed; Probability density function; Ornstein-uhlenbeck process; Memoryless transformation; Wind power (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:213:y:2020:i:c:s0360544220319496

DOI: 10.1016/j.energy.2020.118842

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