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Discussion on A high-resolution bilevel skew-t stochastic generator for assessing Saudi Arabia's wind energy resources

  • Trinity College Dublin

Research output: Contribution to journalArticlepeer-review

Abstract

We provide a detailed discussion on the analysis presented by Tagle and co-authors, who suggested an approach to improve earlier models for handling non-Gaussianity in spatial wind field speed data by simplifying the model formulation to better accommodate large data sets. Our discussion focuses on the energy and socio-economic context of wind potential assessment in Saudi Arabia – an oil-rich country, statistical aspects associated with wind field forecasting, and the prediction of the wind electricity production potential from the wind field forecast.

Original languageBritish English
Article numbere2651
JournalEnvironmetrics
Volume31
Issue number7
DOIs
StatePublished - 1 Nov 2020

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy

Keywords

  • skewed random fields
  • stochastic modeling
  • wind energy

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