An Unsupervised Learning Approach for Predicting Wind Farm Power and Downstream Wakes Using Weather Patterns

An Unsupervised Learning Approach for Predicting Wind Farm Power and Downstream Wakes Using Weather Patterns

Abstract Wind energy resource assessment typically requires numerical modeling at fine resolutions, which is computationally expensive for multi‐year timescales. Increasingly, unsupervised machine learning techniques are used to identify representative weather patterns that can help simulate long‐te...

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Bibliographic Details
Main Authors: Mariana C. A. Clare, Simon C. Warder, Robert Neal, B. Bhaskaran, Matthew D. Piggott
Format: Article
Language:English
Published: American Geophysical Union (AGU) 2024-02-01
Series:Journal of Advances in Modeling Earth Systems
Subjects:
wind energy
cluster analysis
wind resources
wind shadow
weather patterns
atmospheric circulation
Online Access:https://doi.org/10.1029/2023MS003947

Internet

https://doi.org/10.1029/2023MS003947

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