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Enabling Virtual Met Masts for wind energy applications through machinelearning-methods

Energy and AI 2023年第1期11卷 26-42页

作者： Sandra Schwegmann Janosch Faulhaber sebastian pfaffel Zhongjie Yu Martin Dörenkämper Kristian Kersting Julia Gottschall Fraunhofer IWES Küpkersweg 7026129 OldenburgGermany Fraunhofer IEE Königstor 5934119 KasselGermany Technical University of Darmstadt Karolinenplatz 564289 DarmstadtGermany Hessian Center for AI(hessian.AI) Mornewegstraße 3064293 DarmstadtGermany

As wind is the basis of all wind energy projects, a precise knowledge about its availability is needed. For ananalysis of the site-specific wind conditions, Virtual Meteorological Masts (VMMs) are frequently used. VMMsmake use of site calibrated numerical data to provide precise wind estimates during all phases of a wind energyproject. Typically, numerical data are used for the long-term correlation that is required for estimating theyield of new wind farm projects. However, VMMs can also be used to fill data gaps or during the operationalphase as an additional reference data set to detect degrading sensors. The value of a VMM directly dependson its ability and precision to reproduce site-specific environmental conditions. Commonly, linear regressionis used as state of the art to correct reference data to the site-specific conditions. In this study, a frameworkof 10 different machine-learning methods is tested to investigated the benefit of more advanced methods ontwo offshore and one onshore site. We find significantly improving correlations between the VMMs and the reference data when using more advanced methods and present the most promising ones. The K-NearestNeighbors and AdaBoost regressors show the best results in our study, but Multi-Output Mixture of GaussianProcesses is also very promising. The use of more advanced regression models lead to decreased uncertainties;hence those methods should find its way into industrial applications. The recommended regression models canserve as a starting point for the development of end-user applications and services.

关键词： Virtual Met Mast(VMM) Wind power Machine learning Reanalysis data Site assessment Weather Research and Forecasting Model(WRF) Measure-Correlate-Predict(MCP)

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