Articles | Volume 8, issue 5
https://doi.org/10.5194/wes-8-771-2023
https://doi.org/10.5194/wes-8-771-2023
Research article
 | 
17 May 2023
Research article |  | 17 May 2023

Gaussian mixture models for the optimal sparse sampling of offshore wind resource

Robin Marcille, Maxime Thiébaut, Pierre Tandeo, and Jean-François Filipot

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Short summary
A novel data-driven method is proposed to design an optimal sensor network for the reconstruction of offshore wind resources. Based on unsupervised learning of numerical weather prediction wind data, it provides a simple yet efficient method for the siting of sensors, outperforming state-of-the-art methods for this application. It is applied in the main French offshore wind energy development areas to provide guidelines for the deployment of floating lidars for wind resource assessment.
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