Blowing in the wind

Authors

  • Marc Genton,

    1. Professor of Statistics in the Department of Econometrics at the University of Geneva and Associate Professor in the Department of Statistics at Texas A&M University. His research interests include spatiotemporal statistics, robustness, multivariate analysis, skewed multivariate distributions and data mining. This work was partially supported by National Science Foundation grant DMS0504896.
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  • Amanda Hering

    1. Doctoral student working under the supervision of Marc Genton in the Department of Statistics at Texas A&M University on the topic of forecasting wind power. Her research interests include circular statistics and spacetime modelling of environmental data, particularly with nonGaussian distributions.
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Abstract

Part of the answer to rising energy needs and costs may literally be blowing in the wind. Among sustainable sources of electricity, only wind energy has the capacity and technology needed to compete in the open marketplace. The largest onshore wind farm in Europe is being built in Scotland, the largest in the USA is planned for southern California, and the biggest offshore wind farm production in the world is slated for the Thames Estuary. But wind is intermittent. Marc Genton and Amanda Hering explain how advanced statistical techniques will enable wind energy to be more efficiently incorporated into the electrical grid.

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