A Non-Gaussian Spatio-Temporal Model for Daily Wind Speeds Based on a Multi-Variate Skew-\n t\n Distribution
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
KAUST Grant NumberOSR-2015-CRG4-2640
Permanent link to this recordhttp://hdl.handle.net/10754/630312
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AbstractFacing 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.
CitationTagle 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.
SponsorsThis 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.
JournalJournal of Time Series Analysis