March 2024 Sensitivity analysis of wind energy resources with Bayesian non-Gaussian and nonstationary functional ANOVA
Jiachen Zhang, Paola Crippa, Marc G. Genton, Stefano Castruccio
Author Affiliations +
Ann. Appl. Stat. 18(1): 23-41 (March 2024). DOI: 10.1214/23-AOAS1770


The transition from nonrenewable to renewable energies represents a global societal challenge, and developing a sustainable energy portfolio is an especially daunting task for developing countries where little to no information is available regarding the abundance of renewable resources such as wind. Weather model simulations are key to obtain such information when observational data are scarce and sparse over a country as large and geographically diverse as Saudi Arabia. However, output from such models is uncertain, as it depends on inputs such as the parametrization of the physical processes and the spatial resolution of the simulated domain. In such situations a sensitivity analysis must be performed, and the input may have a spatially heterogeneous influence of wind. In this work we propose a latent Gaussian functional analysis of variance (ANOVA) model that relies on a nonstationary Gaussian Markov random field approximation of a continuous latent process. The proposed approach is able to capture the local sensitivity of Gaussian and non-Gaussian wind characteristics such as speed and threshold exceedances over a large simulation domain, and a continuous underlying process also allows us to assess the effect of different spatial resolutions. Our results indicate that: (1) the nonlocal planetary boundary layer scheme and high spatial resolution are both instrumental in capturing wind speed and energy (especially over complex mountainous terrain), and (2) the impact of planetary boundary layer scheme and resolution on Saudi Arabia’s planned wind farms is small (at most 1.4%). Thus, our results lend support for the construction of these wind farms in the next decade.

Funding Statement

This study is based upon work supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award ORFS-2022-CRG11-5069.2. Partial support for this work is provided by the National Science Foundation Grant No. DMS-2014166 to SC and by National Aeronautics and Space Administration under Grant No. 80NSSC21K1490 to SC.


Download Citation

Jiachen Zhang. Paola Crippa. Marc G. Genton. Stefano Castruccio. "Sensitivity analysis of wind energy resources with Bayesian non-Gaussian and nonstationary functional ANOVA." Ann. Appl. Stat. 18 (1) 23 - 41, March 2024.


Received: 1 December 2021; Revised: 1 April 2023; Published: March 2024
First available in Project Euclid: 31 January 2024

Digital Object Identifier: 10.1214/23-AOAS1770

Keywords: Bayesian hierarchical model , Functional ANOVA , Stochastic partial differential equation , wind energy

Rights: Copyright © 2024 Institute of Mathematical Statistics


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Vol.18 • No. 1 • March 2024
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