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Title: Determining variabilities of non-Gaussian wind-speed distributions using different metrics and timescales

Quantification of long-term wind-speed variability is a critical component in wind resource assessment, and effective wind-farm operations require proper assessment of this variability. Yet, wind-speed variations differ across averaging temporal scales because hourly mean wind speeds fluctuate more than yearly averages. In this study, we quantify the influence of averaging timescale to the resultant variability. We assess three spread metrics (standard deviation, coefficient of variation, and robust coefficient of variation) and two distribution measures (skewness and kurtosis) based on 38 years of wind speeds from the National Aeronautics and Space Administration's MERRA-2 reanalysis data set over the contiguous United States. The spatial distributions of wind-speed variability differ with metrics and timescales: wind speeds of fine temporal resolution generate strong variabilities that dilute spatial contrasts; small sample size becomes a constraint in calculating interannual variabilities via annual means and leads to inaccurate results. Overall, we find that metrics based on monthly data portray the largest spatial differences of wind-speed variability. Although standard deviation yields consistent geographical projections, none of the wind-speed data of any time frame are perfectly Gaussian. Furthermore, the robust coefficient of variation, a statistically robust and resistant approach, appears to be the ideal metric for quantifying wind-speed variabilitiesmore » based on monthly mean data.« less
Authors:
 [1] ;  [2] ;  [1] ;  [2]
  1. Univ. of Colorado, Boulder, CO (United States); National Renewable Energy Lab. (NREL), Golden, CO (United States)
  2. National Renewable Energy Lab. (NREL), Golden, CO (United States)
Publication Date:
Report Number(s):
NREL/JA-5000-71355
Journal ID: ISSN 1742-6588
Grant/Contract Number:
AC36-08GO28308
Type:
Accepted Manuscript
Journal Name:
Journal of Physics. Conference Series
Additional Journal Information:
Journal Volume: 1037; Journal ID: ISSN 1742-6588
Publisher:
IOP Publishing
Research Org:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Wind and Water Technologies Office (EE-4W)
Country of Publication:
United States
Language:
English
Subject:
17 WIND ENERGY; higher order statistics; statistics; torque; wind power
OSTI Identifier:
1462464

Lee, Joseph Cheuk-Yi, Fields, Michael J., Lundquist, Julie K., and Lunacek, Monte S.. Determining variabilities of non-Gaussian wind-speed distributions using different metrics and timescales. United States: N. p., Web. doi:10.1088/1742-6596/1037/7/072038.
Lee, Joseph Cheuk-Yi, Fields, Michael J., Lundquist, Julie K., & Lunacek, Monte S.. Determining variabilities of non-Gaussian wind-speed distributions using different metrics and timescales. United States. doi:10.1088/1742-6596/1037/7/072038.
Lee, Joseph Cheuk-Yi, Fields, Michael J., Lundquist, Julie K., and Lunacek, Monte S.. 2018. "Determining variabilities of non-Gaussian wind-speed distributions using different metrics and timescales". United States. doi:10.1088/1742-6596/1037/7/072038. https://www.osti.gov/servlets/purl/1462464.
@article{osti_1462464,
title = {Determining variabilities of non-Gaussian wind-speed distributions using different metrics and timescales},
author = {Lee, Joseph Cheuk-Yi and Fields, Michael J. and Lundquist, Julie K. and Lunacek, Monte S.},
abstractNote = {Quantification of long-term wind-speed variability is a critical component in wind resource assessment, and effective wind-farm operations require proper assessment of this variability. Yet, wind-speed variations differ across averaging temporal scales because hourly mean wind speeds fluctuate more than yearly averages. In this study, we quantify the influence of averaging timescale to the resultant variability. We assess three spread metrics (standard deviation, coefficient of variation, and robust coefficient of variation) and two distribution measures (skewness and kurtosis) based on 38 years of wind speeds from the National Aeronautics and Space Administration's MERRA-2 reanalysis data set over the contiguous United States. The spatial distributions of wind-speed variability differ with metrics and timescales: wind speeds of fine temporal resolution generate strong variabilities that dilute spatial contrasts; small sample size becomes a constraint in calculating interannual variabilities via annual means and leads to inaccurate results. Overall, we find that metrics based on monthly data portray the largest spatial differences of wind-speed variability. Although standard deviation yields consistent geographical projections, none of the wind-speed data of any time frame are perfectly Gaussian. Furthermore, the robust coefficient of variation, a statistically robust and resistant approach, appears to be the ideal metric for quantifying wind-speed variabilities based on monthly mean data.},
doi = {10.1088/1742-6596/1037/7/072038},
journal = {Journal of Physics. Conference Series},
number = ,
volume = 1037,
place = {United States},
year = {2018},
month = {6}
}