IDEAS home Printed from https://ideas.repec.org/a/bla/jtsera/v40y2019i3p312-326.html
   My bibliography  Save this article

A Non‐Gaussian Spatio‐Temporal Model for Daily Wind Speeds Based on a Multi‐Variate Skew‐t Distribution

Author

Listed:
  • Felipe Tagle
  • Stefano Castruccio
  • Paola Crippa
  • Marc G. Genton

Abstract

Facing increasing domestic energy consumption from population growth and industrialization, Saudi Arabia is aiming to reduce its reliance on fossil fuels and to broaden its energy mix by expanding investment in renewable energy sources, including wind energy. A preliminary task in the development of wind energy infrastructure is the assessment of wind energy potential, a key aspect of which is the characterization of its spatio‐temporal behavior. In this study we examine the impact of internal climate variability on seasonal wind power density fluctuations over Saudi Arabia using 30 simulations from the Large Ensemble Project (LENS) developed at the National Center for Atmospheric Research. Furthermore, a spatio‐temporal model for daily wind speed is proposed with neighbor‐based cross‐temporal dependence, and a multi‐variate skew‐t distribution to capture the spatial patterns of higher‐order moments. The model can be used to generate synthetic time series over the entire spatial domain that adequately reproduce the internal variability of the LENS dataset.

Suggested Citation

  • Felipe Tagle & Stefano Castruccio & Paola Crippa & Marc G. Genton, 2019. "A Non‐Gaussian Spatio‐Temporal Model for Daily Wind Speeds Based on a Multi‐Variate Skew‐t Distribution," Journal of Time Series Analysis, Wiley Blackwell, vol. 40(3), pages 312-326, May.
    • Handle: RePEc:bla:jtsera:v:40:y:2019:i:3:p:312-326
    DOI: 10.1111/jtsa.12437
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/jtsa.12437
    Download Restriction: no
    File URL: https://libkey.io/10.1111/jtsa.12437?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
    ---><---

    Citations

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


    Cited by:

    1. Indranil Sahoo & Joseph Guinness & Brian J. Reich, 2023. "Estimating atmospheric motion winds from satellite image data using space‐time drift models," Environmetrics, John Wiley & Sons, Ltd., vol. 34(8), December.
    2. Huang Huang & Stefano Castruccio & Marc G. Genton, 2022. "Forecasting high‐frequency spatio‐temporal wind power with dimensionally reduced echo state networks," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(2), pages 449-466, March.
    3. Crippa, Paola & Alifa, Mariana & Bolster, Diogo & Genton, Marc G. & Castruccio, Stefano, 2021. "A temporal model for vertical extrapolation of wind speed and wind energy assessment," Applied Energy, Elsevier, vol. 301(C).
    4. Yuan Yan & Hsin-Cheng Huang & Marc G. Genton, 2021. "Vector Autoregressive Models with Spatially Structured Coefficients for Time Series on a Spatial Grid," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 26(3), pages 387-408, September.
    5. Giani, Paolo & Tagle, Felipe & Genton, Marc G. & Castruccio, Stefano & Crippa, Paola, 2020. "Closing the gap between wind energy targets and implementation for emerging countries," Applied Energy, Elsevier, vol. 269(C).
    6. Lee, Sharon X. & McLachlan, Geoffrey J., 2021. "On formulations of skew factor models: Skew factors and/or skew errors," Statistics & Probability Letters, Elsevier, vol. 168(C).
    7. Felipe Tagle & Marc G. Genton & Andrew Yip & Suleiman Mostamandi & Georgiy Stenchikov & Stefano Castruccio, 2020. "Rejoinder to the discussion on A high‐resolution bilevel skew‐t stochastic generator for assessing Saudi Arabia's wind energy resources," Environmetrics, John Wiley & Sons, Ltd., vol. 31(7), November.
    8. Santos-Fernandez, Edgar & Ver Hoef, Jay M. & Peterson, Erin E. & McGree, James & Isaak, Daniel J. & Mengersen, Kerrie, 2022. "Bayesian spatio-temporal models for stream networks," Computational Statistics & Data Analysis, Elsevier, vol. 170(C).

    More about this item

    Statistics

    Access and download statistics

    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:bla:jtsera:v:40:y:2019:i:3:p:312-326. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0143-9782 .

    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.