Influence of the data sampling interval in the estimation of the parameters of the Weibull wind speed probability density distribution: a case study

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Weibull Parameters Estimation Using Four Different Methods and Most Energy-Carrying Wind Speed Analysis (2011, background): “Under the assumption that air density is independent of wind speed ( Hennessey 1977 ; Jamil 1994 ; Manwell et al, 2002 ; Ramirez and Carta 2005 ), WPD has been estimated in the scientific literature.”
Current methods and advances in forecasting of wind power generation (2012, unknown): “In Ramìrez and Carta [65], it was shown that, the use of autocorrelated (and thus not independent) successive hourly mean wind speeds, though invalidating all of the usual statistical tests, has no appreciable effect on the shape of the pdf estimated from the data.”
Short‐Term Wind Speed Forecasting for Power System Operations (2012, background): “…accepted for wind energy: it is flexible with a closed form, only has two parameters that are easy to estimate, and has specific goodness-of-fit tests as discussed by Ramı́rez & Carta (2005) who also pointed out that the data sampling interval has no significant effect on the shape of the density.”
Comparison of four methods for estimating the Weibull distribution parameters (2014, methodology): “The starting point we use according to Ramírez and Carta (2005) 1.086 2 , x xc s (25) where 1 1 n i i x x n is the sample mean and 22 1 1 1 n x i i s x x n is the sample variance.”