Posters

Abstract

The statistical characterization of intra-10-minute wind fluctuations remains poorly documented. This study presents a comprehensive empirical analysis of wind speed distributions within 10-minute averaging periods using high-frequency (1 Hz) measurements from diverse sites including offshore platforms, complex terrain, and urban environments. Contrary to the commonly assumed Gaussian distribution, wind speed within 10-minute windows exhibits systematic skewness that varies with mean wind velocity. Statistical analysis reveals distributions become increasingly left-skewed with rising wind speed, while lower velocity regimes display pronounced right-skewness. To capture this asymmetric behavior, seven candidate probability distributions are evaluated: Weibull, Gamma, Nakagami, and Rice for right-skewed regimes; Normal, Logistic, and Student's t for symmetric cases; and mirrored versions of Gamma, Nakagami, and Rice distributions for left-skewed behavior (obtained by reflecting the parent distribution about a pivot point to represent upper-bounded wind speed fluctuations). Distribution fitting identifies regime-specific optimal models: Gamma distributions for right-skewed cases (mean KS statistic: 0.071), Normal for symmetric regimes (KS: 0.058), and mirrored Rice for left-skewed behavior (KS: 0.070), each providing significant improvements over uniform Gaussian assumptions. The analysis quantifies goodness-of-fit across wind speed bins, revealing improved representation at higher velocities where mechanical turbulence dominates. The findings establish an empirical foundation for refined wind variability modeling and challenge widespread Gaussian assumptions in short-term wind energy assessment.  Keywords: Wind speed variability, statistical distributions, skewness, 10-minute averaging, high-frequency measurements, turbulence characterization, wind energy