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

Felipe Tagle, Stefano Castruccio, Paola Crippa, Marc G. Genton

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

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.
Original languageEnglish (US)
Pages (from-to)312-326
Number of pages15
JournalJournal of Time Series Analysis
Volume40
Issue number3
DOIs
StatePublished - Dec 3 2018

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