Wind speed modeling in wind energy based on one year of SCADA data using statistical distribution functions

Wind speed is one of the basic meteorological parameters in the field of wind energy, as it directly affects the efficiency and output power of wind turbines. Wind speed distribution is most often modeled using statistical functions, in particular the probability density function (PDF) and the cumulative distribution function (CDF). In this paper, a comparison of several statistical distributions (Weibull, Rayleigh, gamma, normal and beta) was performed through the analysis of the empirical distribution of wind speed recorded over a one-year period. For all distributions, the root mean square error (RMSE) and the mean absolute error (MAE) between observed and modeled values were calculated, and the smallest values of these criteria were used to identify the model that most accurately describes the actual wind behavior at the observed location. The obtained results enable a more precise assessment of the wind speed distribution and the energy potential of the location, which is of key importance for optimizing the operation of wind energy systems.