A Non-Gaussian Spatio-Temporal Model for Daily Wind Speeds Based on a Multi-Variate Skew-\n t\n Distribution
Type
ArticleKAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionStatistics Program
KAUST Grant Number
OSR-2015-CRG4-2640Date
2018-12-03Permanent link to this record
http://hdl.handle.net/10754/630312
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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
WileyJournal
Journal of Time Series AnalysisAdditional Links
https://onlinelibrary.wiley.com/doi/full/10.1111/jtsa.12437ae974a485f413a2113503eed53cd6c53
10.1111/jtsa.12437