Saudi Arabia has been trying to change its long tradition of relying on fossil fuels
and seek renewable energy sources such as wind power. In this thesis, I firstly provide
a comprehensive assessment of wind energy resources and associated spatio-temporal
patterns over Saudi Arabia in both current and future climate conditions, based on a
Regional Climate Model output. A high wind energy potential exists and is likely to
persist at least until 2050 over a vast area ofWestern Saudi Arabia, particularly in the
region between Medina and the Red Sea coast and during Summer months. Since an
accurate assessment of wind extremes is crucial for risk management purposes, I then
present the first high-resolution risk assessment of wind extremes over Saudi Arabia.
Under the Bayesian framework, I measure the uncertainty of return levels and produce
risk maps of wind extremes, which show that locations in the South of Saudi
Arabia and near the Red Sea and the Persian Gulf are at very high risk of disruption
of wind turbine operations. In order to perform spatial predictions of the bivariate
wind random field for efficient turbine control, I propose parametric variogram matrix
(function) models for cokriging, which have the advantage of allowing for a smooth
transition between a joint second-order and intrinsically stationary vector random
field. Under Gaussianity, the covariance function is central to spatio-temporal modeling,
which is useful to understand the dynamics of winds in space and time. I review
the various space-time covariance structures and models, some of which are visualized
with animations, and associated tests. I also discuss inference issues and a case study based on a high-resolution wind-speed dataset. The Gaussian assumption commonly
made in statistics needs to be validated, and I show that tests for independently and
identically distributed data cannot be used directly for spatial data. I then propose a
new multivariate test for spatial data by accounting for the spatial dependence. The
new test is easy to compute, has a chi-square null distribution, and has a good control
of the type I error and a high empirical power.
| Identifer | oai:union.ndltd.org:kaust.edu.sa/oai:repository.kaust.edu.sa:10754/665850 |
| Date | 08 November 2020 |
| Creators | Chen, Wanfang |
| Contributors | Genton, Marc G., Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Huser, Raphaël G., Stenchikov, Georgiy L., Zhang, Hao |
| Source Sets | King Abdullah University of Science and Technology |
| Language | English |
| Detected Language | English |
| Type | Dissertation |
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