A Non-Gaussian Spatio-Temporal Model for Daily Wind Speeds Based on a Multivariate Skew-t Distribution

TL;DR

This paper introduces a novel spatio-temporal model for daily wind speeds using a multivariate skew-t distribution, effectively capturing complex spatial patterns and internal climate variability for renewable energy assessment.

Contribution

It proposes a new non-Gaussian spatio-temporal model incorporating neighbor-based dependence and skew-t distribution, improving wind speed simulation accuracy.

Findings

Model reproduces internal climate variability.

Captures higher order spatial moments.

Generates realistic synthetic wind speed series.

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 multivariate 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.