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 language | British English |
|---|---|
| Article number | e2651 |
| Journal | Environmetrics |
| Volume | 31 |
| Issue number | 7 |
| DOIs | |
| State | Published - 1 Nov 2020 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
-
SDG 7 Affordable and Clean Energy
Keywords
- skewed random fields
- stochastic modeling
- wind energy
Fingerprint
Dive into the research topics of 'Discussion on A high-resolution bilevel skew-t stochastic generator for assessing Saudi Arabia's wind energy resources'. Together they form a unique fingerprint.Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver