Articles | Volume 3, issue 2
https://doi.org/10.5194/wes-3-845-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-3-845-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
05 Nov 2018
Research article |
| 05 Nov 2018
| 05 Nov 2018
Assessing variability of wind speed: comparison and validation of 27 methodologies
Joseph C. Y. Lee
CORRESPONDING AUTHOR
National Renewable Energy Laboratory, Golden, CO 80401, USA
Department of Atmospheric and Oceanic Sciences, University of Colorado Boulder, Boulder, CO 80309, USA
M. Jason Fields
National Renewable Energy Laboratory, Golden, CO 80401, USA
Julie K. Lundquist
National Renewable Energy Laboratory, Golden, CO 80401, USA
Department of Atmospheric and Oceanic Sciences, University of Colorado Boulder, Boulder, CO 80309, USA
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18 citations as recorded by crossref.
- Contrasting hydrodynamic regimes of submerged pinnacle and emergent coral reefs G. Galbraith et al. 10.1371/journal.pone.0273092
- Exploiting offshore wind and solar resources in the Mediterranean using ERA5 reanalysis data T. Soukissian et al. 10.1016/j.enconman.2021.114092
- Feature selection and response prediction on a suspension bridge due to wind effect by machine learning A. Afshar et al. 10.1016/j.istruc.2024.107945
- Analysis of short-term wind speed variation, trends and prediction: A case study of Tamil Nadu, India R. Kaja Bantha Navas et al. 10.1515/jisys-2023-0051
- Confidence Intervals for the Mean and Difference of Means of Birnbaum-Saunders Distributions with Application to Wind Speed Data N. Ratasukharom et al. 10.37394/23206.2024.23.54
- High-Resolution Wind Speed Estimates for the Eastern Mediterranean Basin: A Statistical Comparison Against Coastal Meteorological Observations S. Hadjipetrou & P. Kyriakidis 10.3390/wind4040016
- Inter Annual Variability of wind speed in India J. Bastin et al. 10.1080/01430750.2021.1945492
- Evaluating vector winds over eastern China in 2022 predicted by the CMA-MESO model and ECMWF forecast F. Huang et al. 10.1016/j.aosl.2024.100559
- Geophysical constraints on the reliability of solar and wind power worldwide D. Tong et al. 10.1038/s41467-021-26355-z
- Mitigation of offshore wind power intermittency by interconnection of production sites I. Solbrekke et al. 10.5194/wes-5-1663-2020
- Hybrid neurofuzzy investigation of short-term variability of wind resource in site suitability analysis: a case study in South Africa P. Adedeji et al. 10.1007/s00521-021-06001-x
- Future changes in wind energy potential over China using RegCM4 under RCP emission scenarios J. Wu et al. 10.1016/j.accre.2021.06.005
- Assessment of wind energy potential in Zambia G. Mwandila et al. 10.1016/j.esd.2024.101375
- Long-Term Assessment of Morocco’s Offshore Wind Energy Potential Using ERA5 and IFREMER Wind Data Y. Zekeik et al. 10.3390/jmse12030460
- How many offshore wind turbines does New England need? H. Livingston & J. Lundquist 10.1002/met.1969
- An overview of wind-energy-production prediction bias, losses, and uncertainties J. Lee & M. Fields 10.5194/wes-6-311-2021
- JRA55 is the best reanalysis representing observed near-surface wind speeds over India A. Das & S. Baidya Roy 10.1016/j.atmosres.2023.107111
- Wind power density characterization in arid and semi-arid Taita-Taveta and Garissa counties of Kenya I. Rotich & P. Musyimi 10.1016/j.clet.2023.100704
1 citations as recorded by crossref.
Latest update: 14 Dec 2024
Short summary
To find the ideal way to quantify long-term wind-speed variability, we compare 27 metrics using 37 years of wind and energy data. We conclude that the robust coefficient of variation can effectively assess and correlate wind-speed and energy-production variabilities. We derive adequate results via monthly mean data, whereas uncertainty arises in interannual variability calculations. We find that reliable estimates of wind-speed variability require 10 ± 3 years of monthly mean wind data.
To find the ideal way to quantify long-term wind-speed variability, we compare 27 metrics using...
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