IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v149y2020icp861-876.html
   My bibliography  Save this article

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

Author

Listed:
  • 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.

Suggested Citation

  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148119319135
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2019.12.041?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Bahaj, AbuBakr S., 2011. "Generating electricity from the oceans," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(7), pages 3399-3416, September.
    2. Chen, Falin, 2010. "Kuroshio power plant development plan," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(9), pages 2655-2668, December.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    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. 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.
    10. 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.
    11. 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.
    12. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Katikas, Loukas & Dimitriadis, Panayiotis & Koutsoyiannis, Demetris & Kontos, Themistoklis & Kyriakidis, Phaedon, 2021. "A stochastic simulation scheme for the long-term persistence, heavy-tailed and double periodic behavior of observational and reanalysis wind time-series," Applied Energy, Elsevier, vol. 295(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jiang, Haiyan & Wang, Jianzhou & Wu, Jie & Geng, Wei, 2017. "Comparison of numerical methods and metaheuristic optimization algorithms for estimating parameters for wind energy potential assessment in low wind regions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 1199-1217.
    2. Soukissian, Takvor H. & Karathanasi, Flora E., 2017. "On the selection of bivariate parametric models for wind data," Applied Energy, Elsevier, vol. 188(C), pages 280-304.
    3. Emilio Gómez-Lázaro & María C. Bueso & Mathieu Kessler & Sergio Martín-Martínez & Jie Zhang & Bri-Mathias Hodge & Angel Molina-García, 2016. "Probability Density Function Characterization for Aggregated Large-Scale Wind Power Based on Weibull Mixtures," Energies, MDPI, vol. 9(2), pages 1-15, February.
    4. Allouhi, A. & Zamzoum, O. & Islam, M.R. & Saidur, R. & Kousksou, T. & Jamil, A. & Derouich, A., 2017. "Evaluation of wind energy potential in Morocco's coastal regions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 311-324.
    5. Wais, Piotr, 2017. "A review of Weibull functions in wind sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 1099-1107.
    6. Mazzeo, Domenico & Oliveti, Giuseppe & Labonia, Ester, 2018. "Estimation of wind speed probability density function using a mixture of two truncated normal distributions," Renewable Energy, Elsevier, vol. 115(C), pages 1260-1280.
    7. Wang, Jianzhou & Hu, Jianming & Ma, Kailiang, 2016. "Wind speed probability distribution estimation and wind energy assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 881-899.
    8. Jung, Christopher & Schindler, Dirk, 2019. "Wind speed distribution selection – A review of recent development and progress," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    9. Wais, Piotr, 2017. "Two and three-parameter Weibull distribution in available wind power analysis," Renewable Energy, Elsevier, vol. 103(C), pages 15-29.
    10. Hu, Qinghua & Wang, Yun & Xie, Zongxia & Zhu, Pengfei & Yu, Daren, 2016. "On estimating uncertainty of wind energy with mixture of distributions," Energy, Elsevier, vol. 112(C), pages 935-962.
    11. Dongbum Kang & Kyungnam Ko & Jongchul Huh, 2018. "Comparative Study of Different Methods for Estimating Weibull Parameters: A Case Study on Jeju Island, South Korea," Energies, MDPI, vol. 11(2), pages 1-19, February.
    12. Guedes, Kevin S. & de Andrade, Carla F. & Rocha, Paulo A.C. & Mangueira, Rivanilso dos S. & de Moura, Elineudo P., 2020. "Performance analysis of metaheuristic optimization algorithms in estimating the parameters of several wind speed distributions," Applied Energy, Elsevier, vol. 268(C).
    13. Arslan, Talha & Bulut, Y. Murat & Altın Yavuz, Arzu, 2014. "Comparative study of numerical methods for determining Weibull parameters for wind energy potential," Renewable and Sustainable Energy Reviews, Elsevier, vol. 40(C), pages 820-825.
    14. Roger Samsó & Júlia Crespin & Antonio García-Olivares & Jordi Solé, 2023. "Examining the Potential of Marine Renewable Energy: A Net Energy Perspective," Sustainability, MDPI, vol. 15(10), pages 1-35, May.
    15. Kantar, Yeliz Mert & Usta, Ilhan & Arik, Ibrahim & Yenilmez, Ismail, 2018. "Wind speed analysis using the Extended Generalized Lindley Distribution," Renewable Energy, Elsevier, vol. 118(C), pages 1024-1030.
    16. Estefania Artigao & Antonio Vigueras-Rodríguez & Andrés Honrubia-Escribano & Sergio Martín-Martínez & Emilio Gómez-Lázaro, 2021. "Wind Resource and Wind Power Generation Assessment for Education in Engineering," Sustainability, MDPI, vol. 13(5), pages 1-27, February.
    17. Jin, Jingliang & Zhou, Dequn & Zhou, Peng & Miao, Zhuang, 2014. "Environmental/economic power dispatch with wind power," Renewable Energy, Elsevier, vol. 71(C), pages 234-242.
    18. Alrashidi, Musaed & Rahman, Saifur & Pipattanasomporn, Manisa, 2020. "Metaheuristic optimization algorithms to estimate statistical distribution parameters for characterizing wind speeds," Renewable Energy, Elsevier, vol. 149(C), pages 664-681.
    19. Lepore, Antonio & Palumbo, Biagio & Pievatolo, Antonio, 2020. "A Bayesian approach for site-specific wind rose prediction," Renewable Energy, Elsevier, vol. 150(C), pages 691-702.
    20. Chang, Tian Pau, 2011. "Estimation of wind energy potential using different probability density functions," Applied Energy, Elsevier, vol. 88(5), pages 1848-1856, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:renene:v:149:y:2020:i:c:p:861-876. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.