Evaluating the Methodologies of 

Assessing Long-Term Variability 

of Wind Speed

Joseph C. Y. Lee, M. Jason Fields, Julie K. Lundquist

European Meteorological Society Annual Meeting 2018

Budapest, Hungary

September 7, 2018
NREL | 2

How Would One Quantify the Variability of Wind Speed?

Monthly mean wind speed (m s-1)

Relative frequency

71% data 

within 1 σ

Monthly mean wind speed (m s-1)

Relative frequency

• 37-year monthly mean 

wind speed at a sample 

site in Texas

• Close to Gaussian

• Using standard deviation 

(σ) seems appropriate

• 37-year wind speed at a 

sample site in Oregon 

• Skewed distribution

TX

OR

Gaussian: 

68% data 

within 1 σ

65% data 

within 1 σ
NREL | 3

How Would One Quantify the Variability of Wind Speed?

Need a statistically robust 

and resistant method

Monthly mean wind speed (m s-1)

Relative frequency

Monthly mean wind speed (m s-1)

Relative frequency

TX

OR

Gaussian: 

68% data 

within 1 σ

65% data 

within 1 σ

71% data 

within 1 σ
NREL | 4

The Importance of Quantifying Variability

• Project lifetime variability can contribute 10% of total project 

uncertainty1.

1 Clifton, A., A. Smith, and M. Fields. 2016. Wind Plant Preconstruction Energy Estimates: Current Practice and Opportunities (Technical Report). TP-5000-64735. National 

Renewable Energy Laboratory (NREL), Golden, CO (US). https://www.nrel.gov/docs/fy16osti/64735.pdf.
NREL | 5

The Importance of Quantifying Variability

• Project lifetime variability can contribute 10% of total project 

uncertainty1

• Representing variability as standard deviation is problematic

• Numerous spread metrics exist. 

1 Clifton, A., A. Smith, and M. Fields. 2016. Wind Plant Preconstruction Energy Estimates: Current Practice and Opportunities (Technical Report). TP-5000-64735. National 

Renewable Energy Laboratory (NREL), Golden, CO (US). https://www.nrel.gov/docs/fy16osti/64735.pdf.

SSSSSSSSSSSSSSSS dddddd dddd =

1

− 1�

ii=1

nn

xxii − xx̅ 2

CC CCCC CC VV CC =

ss dddddd dddd

mm

II IIII II rr IIIIII

= 0.75 − 0.25

MM dddddd dddd mm = (xx − xx̅)

= max xx − min(xx)

WW WW pp
NREL | 6

• Project lifetime variability can contribute 10% of total project 

uncertainty1

• Representing variability as standard deviation is problematic

• Numerous spread metrics exist

• We identified the robust coefficient of variation (RCoV), a 

statistically robust and resistant way to quantify variability.

1 Clifton, A., A. Smith, and M. Fields. 2016. Wind Plant Preconstruction Energy Estimates: Current Practice and Opportunities (Technical Report). TP-5000-64735. National 

Renewable Energy Laboratory (NREL), Golden, CO (US). https://www.nrel.gov/docs/fy16osti/64735.pdf.

=

mm |xx − mm (xx)|

mm (xx)

The Importance of Quantifying Variability
NREL | 7

Goal: Compare Spread Metrics and Determine the Best One

MERRA-22 80 m 

wind speed, 1980 

– 2016 (37 years)

Energy Information 

Administration (EIA) net monthly 

generations (600+ sites)

Site-specific linear 

regression, chose 

sites with R2 > 0.75

~200 sites chosen, including 

the Oregon and Texas sites

Tested 27 spread metrics 

and found RCoV is the most 

robust method

2Gelaro, R. et al. 2017. “The 

Modern-Era Retrospective 

Analysis for Research and 

Applications, Version 2 

(MERRA-2).” Journal of 

Climate. 

https://doi.org/10.1175/JC

LI-D-16-0758.1. 

Variability
NREL | 8

RCoV Has More Predictive Power Than Standard Deviation

• Correlate wind speed RCoV and energy production RCoV, r = 0.856

– Strong r: high wind-speed variability translates into high-energy variability

• Using standard deviation only yields correlation of 0.184.
NREL | 9

Including More Years in Estimating RCoV Reduces Variations

The period required to calculate wind-speed RCoV with 90% confidence is 10 ± 3 years. Wind

-speed RCoV

OR site
NREL | 10

Ideal Wind Sites Possess Strong Winds with Low Variability

Low Variability High Variability Calm Windy
NREL | 11

Ideal Wind Sites Possess Strong Winds With Low Variability
Low Variability High Variability Calm Windy NREL | 12

Ideal European Wind Locations Surround the North Sea
NREL | 13

Ideal European Wind Locations Surround the North Sea
NREL | 14

Non-Gaussian 

wind speed 

distributions 

require robust 

methods to 

quantify 

variability 

After we 

challenged 27 

spread metrics 

with 200 sites 

over 37 years, 

we recommend 

RCoV: 

MERRA-2 winds 

and EIA energy 

data yield strong 

correlations 

between 

wind-speed RCoV 

and 

energy-production 

RCoV

Assessing 

variability via 

RCoV requires 

10 ± 3 years of 

wind speed 

data

Windy regions 

with low 

variability 

produce stable 

generations, 

(e.g., areas 

around the North 

Sea)

Recommended Best Practices

=

mm |xx − mm (xx)|

mm (xx)
www.nrel.gov

Joseph Lee

National Wind Technology Center

joseph.lee@nrel.gov

NREL/PR-5000-72307

Lee, J.C.Y., M.J. Fields, and J.K. Lundquist. 2018. (In review). “Assessing Variability 

of Wind Speed: Comparison and Validation of 27 Methodologies.” Wind Energy 

Science discussion. https://doi.org/10.5194/wes-2018-49. 

Lee, J.C.Y., M.J. Fields, and J.K. Lundquist. 2018. “Determining variabilities of nonGaussian wind-speed distributions using different metrics and timescales.” The 

Science of Making Torque from Wind (TORQUE 2018). Doi: 10.1088/1742-

6596/1037/7/072038. 

This work was authored by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the U.S. 

Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding provided by the U.S. Department of Energy Office of Energy 

Efficiency and Renewable Energy Wind Energy Technologies Office. The views expressed in the article do not necessarily represent the 

views of the DOE or the U.S. Government. The U.S. Government retains and the publisher, by accepting the article for publication, 

acknowledges that the U.S. Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the 

published form of this work, or allow others to do so, for U.S. Government purposes.