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

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    manuscript_v3.pdf
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    Type
    Article
    Authors
    Tagle, Felipe
    Castruccio, Stefano
    Crippa, Paola
    Genton, Marc G. cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Statistics Program
    KAUST Grant Number
    OSR-2015-CRG4-2640
    Date
    2018-12-03
    Permanent link to this record
    http://hdl.handle.net/10754/630312
    
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    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.
    Citation
    Tagle F, Castruccio S, Crippa P, Genton MG (2018) A Non-Gaussian Spatio-Temporal Model for Daily Wind Speeds Based on a Multi-Variate Skew-\n t\n Distribution. Journal of Time Series Analysis. Available: http://dx.doi.org/10.1111/jtsa.12437.
    Sponsors
    This publication is based on work supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No: OSR-2015-CRG4-2640.
    Publisher
    Wiley
    Journal
    Journal of Time Series Analysis
    DOI
    10.1111/jtsa.12437
    Additional Links
    https://onlinelibrary.wiley.com/doi/full/10.1111/jtsa.12437
    ae974a485f413a2113503eed53cd6c53
    10.1111/jtsa.12437
    Scopus Count
    Collections
    Articles; Statistics Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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