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

IF 3.6 Q3 GREEN & SUSTAINABLE SCIENCE & TECHNOLOGY
Robin Marcille, M. Thiébaut, P. Tandeo, J. Filipot
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引用次数: 0

Abstract

Abstract. Wind resource assessment is a crucial step for the development of offshore wind energy. It relies on the installation of measurement devices, whose placement is an open challenge for developers. Indeed, the optimal sensor placement for field reconstruction is an open challenge in the field of sparse sampling. As for the application to offshore wind field reconstruction, no similar study was found, and standard strategies are based on semi-empirical choices. In this paper, a sparse sampling method using a Gaussian mixture model on numerical weather prediction data is developed for offshore wind reconstruction. It is applied to France's main offshore wind energy development areas: Normandy, southern Brittany and the Mediterranean Sea. The study is based on 3 years of Météo-France AROME's data, available through the MeteoNet data set. Using a Gaussian mixture model for data clustering, it leads to optimal sensor 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 field reconstruction. The described method applied to the study area output sensor arrays of respectively seven, four and four sensors for Normandy, southern Brittany and the Mediterranean Sea. Those sensor arrays perform approximately 20 % better than the median Monte Carlo case and more than 30 % better than state-of-the-art methods with regards to wind field reconstruction error.
海上风电资源最优稀疏采样的高斯混合模型
摘要风资源评估是海上风能开发的关键环节。它依赖于测量设备的安装,而测量设备的放置对开发人员来说是一个公开的挑战。事实上,用于场重建的最佳传感器放置在稀疏采样领域是一个公开的挑战。至于在海上风场重建中的应用,没有发现类似的研究,标准策略基于半经验选择。本文提出了一种利用高斯混合模型对数值天气预测数据进行稀疏采样的方法,用于海上风电重建。它适用于法国主要的海上风能开发地区:诺曼底、布列塔尼南部和地中海。该研究基于美泰-法国AROME三年的数据,可通过MeteoNet数据集获得。使用高斯混合模型进行数据聚类,可以得到风场重建误差方面的最佳传感器位置。描述了所提出的工作流程,并将其与最先进的稀疏采样方法进行了比较。它构成了一种稳健而简单的方法,用于定义海上风场重建的最佳传感器选址。所描述的方法应用于研究区域,分别为诺曼底、布列塔尼南部和地中海的七个、四个和四个传感器的输出传感器阵列。这些传感器阵列执行大约20 % 比蒙特卡罗中值情况好,并且超过30 % 在风场重建误差方面优于现有技术的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Wind Energy Science
Wind Energy Science GREEN & SUSTAINABLE SCIENCE & TECHNOLOGY-
CiteScore
6.90
自引率
27.50%
发文量
115
审稿时长
28 weeks
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