Preprints
https://doi.org/10.5194/wes-2022-39
https://doi.org/10.5194/wes-2022-39
 
02 Jun 2022
02 Jun 2022
Status: this preprint is currently under review for the journal WES.

Gaussian Mixture Models for the Optimal Sparse Sampling of Offshore Wind Resource

Robin Marcille1,2, Maxime Thiébaut1, Pierre Tandeo2, and Jean-François Filipot1 Robin Marcille et al.
  • 1France Énergies Marines, Technopôle Brest-Iroise, 525 Avenue Alexis de Rochon, 29280 Plouzané, France
  • 2IMT Atlantique, Lab-STICC, UMR CNRS 6285, 29238 Plouzané, France

Abstract. Offshore wind resource assessment is a crucial step for the development of offshore wind energy. It relies on the installation of measurement devices, which placement is an open challenge for developers. In this paper, a sparse sampling method using a Gaussian Mixture Model on Numerical Weather Prediction data is developed for the offshore wind reconstruction. It is applied on France's main offshore wind energy development areas, Normandy, Southern Brittany, and the Mediterranean Sea. The study is based on 3 years of Meteo France AROME's data, available through the MeteoNet data-set. Using a Gaussian Mixture Model for data clustering, it yields to optimal sensors' locations with regards to wind field reconstruction error. The proposed workflow is described and compared to state-of-the-art methods for sparse sampling. It constitutes a robust yet simple method for the definition of optimal sensor siting for offshore wind reconstruction. The described method yields to optimal network of 7, 4, and 4 sensors for Normandy, Southern Brittany and the Mediterranean Sea with a gain of approximately 20 % in wind field reconstruction error compared to the median Monte Carlo case, and more than 30 % compared to state-of-the-art methods.

Robin Marcille et al.

Status: open (until 27 Jul 2022)

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Robin Marcille et al.

Robin Marcille et al.

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Short summary
A novel data-driven method is proposed to design an optimal sensors network for the reconstruction of offshore wind resource. 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 on the main French offshore wind energy development areas to provide guidelines for the deployment of floating LiDARs for wind resource assessment.