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Statistical tests for the distribution of surface wind and current speeds across the globe

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  • Campisi-Pinto, Salvatore
  • Gianchandani, Kaushal
  • Ashkenazy, Yosef

Abstract

The distribution of surface winds and currents is important from climatic and energy production aspects. It is commonly assumed that the distribution of surface winds and currents speed is Weibull, yet, previous studies indicated that this assumption is not always valid. An inaccurate probability distribution function (PDF) of wind (current) statistic can lead to erroneous power estimation; thus, it is necessary to examine the accuracy of the PDFs employed. We propose statistical tests to check the validity of an assumed distribution of wind and current speeds. The main statistical test can be applied to any distribution and is based on surrogate data where the different moments of the data are compared with the moments of the surrogate data. We applied this and other tests to global surface wind and current speeds and found that the generalized gamma distribution fits the data distributions better than the Weibull distribution. The percentage of locations that fall within the confidence interval of the assumed distribution varies with the moment. The third moment is used to estimate the potential power of winds and currents — we find that 89% (95%) of the wind (current) grid points fall within the 95% confidence interval of the generalized gamma distribution.

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  • Campisi-Pinto, Salvatore & Gianchandani, Kaushal & Ashkenazy, Yosef, 2020. "Statistical tests for the distribution of surface wind and current speeds across the globe," Renewable Energy, Elsevier, vol. 149(C), pages 861-876.
    • Handle: RePEc:eee:renene:v:149:y:2020:i:c:p:861-876
    DOI: 10.1016/j.renene.2019.12.041
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    1. Wu, Jie & Wang, Jianzhou & Chi, Dezhong, 2013. "Wind energy potential assessment for the site of Inner Mongolia in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 21(C), pages 215-228.
    2. Bahaj, AbuBakr S., 2011. "Generating electricity from the oceans," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(7), pages 3399-3416, September.
    3. Chen, Falin, 2010. "Kuroshio power plant development plan," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(9), pages 2655-2668, December.
    4. Kabir, Asif & Lemongo-Tchamba, Ivan & Fernandez, Arturo, 2015. "An assessment of available ocean current hydrokinetic energy near the North Carolina shore," Renewable Energy, Elsevier, vol. 80(C), pages 301-307.
    5. Edenhofer, Ottmar & Kalkuhl, Matthias, 2011. "When do increasing carbon taxes accelerate global warming? A note on the green paradox," Energy Policy, Elsevier, vol. 39(4), pages 2208-2212, April.
    6. Carta, J.A. & Ramírez, P., 2007. "Analysis of two-component mixture Weibull statistics for estimation of wind speed distributions," Renewable Energy, Elsevier, vol. 32(3), pages 518-531.
    7. Akdag, S.A. & Bagiorgas, H.S. & Mihalakakou, G., 2010. "Use of two-component Weibull mixtures in the analysis of wind speed in the Eastern Mediterranean," Applied Energy, Elsevier, vol. 87(8), pages 2566-2573, August.
    8. Lun, Isaac Y.F & Lam, Joseph C, 2000. "A study of Weibull parameters using long-term wind observations," Renewable Energy, Elsevier, vol. 20(2), pages 145-153.
    9. Yang, Xiufeng & Haas, Kevin A. & Fritz, Hermann M., 2014. "Evaluating the potential for energy extraction from turbines in the gulf stream system," Renewable Energy, Elsevier, vol. 72(C), pages 12-21.
    10. Karagali, Ioanna & Badger, Merete & Hahmann, Andrea N. & Peña, Alfredo & B. Hasager, Charlotte & Sempreviva, Anna Maria, 2013. "Spatial and temporal variability of winds in the Northern European Seas," Renewable Energy, Elsevier, vol. 57(C), pages 200-210.
    11. Carta, J.A. & Ramírez, P. & Velázquez, S., 2009. "A review of wind speed probability distributions used in wind energy analysis: Case studies in the Canary Islands," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(5), pages 933-955, June.
    12. Lo Brano, Valerio & Orioli, Aldo & Ciulla, Giuseppina & Culotta, Simona, 2011. "Quality of wind speed fitting distributions for the urban area of Palermo, Italy," Renewable Energy, Elsevier, vol. 36(3), pages 1026-1039.
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