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Lookup NU author(s): Dr Jack Kennedy, Dr Daniel Henderson, Professor Kevin Wilson
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Renewable energy projects, such as large offshore wind farms, are criticalto achieving low-emission targets set by governments. Stochastic computer modelsallow us to explore future scenarios to aid decision making whilst consideringthe most relevant uncertainties. Complex stochastic computer models can be prohibitivelyslow and thus an emulator may be constructed and deployed to allow forefficient computation. We present a novel heteroscedastic Gaussian Process emulatorwhich exploits cheap approximations to a stochastic offshore wind farm simulator.We also conduct a probabilistic sensitivity analysis to understand the influence of keyparameters in the wind farm model which will help us to plan a probability elicitationin the future.
Author(s): Kennedy JC, Henderson DA, Wilson KJ
Publication type: Article
Publication status: Published
Journal: Journal of the Royal Statistical Society, Series C: Applied Statistics
Year: 2023
Volume: 72
Issue: 3
Pages: 608-627
Print publication date: 01/06/2023
Online publication date: 21/03/2023
Acceptance date: 24/02/2023
Date deposited: 01/12/2022
ISSN (print): 0035-9254
ISSN (electronic): 1467-9876
Publisher: Oxford University Press
URL: https://doi.org/10.1093/jrsssc/qlad023
DOI: 10.1093/jrsssc/qlad023
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