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AGILE 2015 pp 165-180 | Cite as

Statistical Learning Approach for Wind Speed Distribution Mapping: The UK as a Case Study

  • Fabio Veronesi
  • Stefano Grassi
  • Martin Raubal
  • Lorenz Hurni
Chapter
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)

Abstract

Wind resource assessment is fundamental when selecting a site for wind energy projects. Wind speed is influenced by a plethora of environmental factors and understanding its spatial variability is key for determining the economic viability of a site. Deterministic estimation methods, which are based on physics, represent the industry standard in wind-speed mapping. Over the years, these methods have proven capable of estimating wind speed with a relatively high accuracy. However measuring stations, which provide the starting data for all wind speed estimations, are often located at a distance from each other, in some cases, tens of kilometres or more. This adds an unavoidable level of uncertainty to the estimates, which deterministic methods fail to take into account. For this reason, even though there are ways of determining the overall uncertainty of the estimation, e.g. cross-validation, deterministic methods do not provide means of assessing the site-specific uncertainty. This paper introduces a statistical method for estimating wind speed, based on spatial statistics. In particular, we present a statistical learning approach, based on ensembles of regression trees, to estimate both the wind distribution in specific locations and to assess the site-specific uncertainty.

Keywords

Wind speed Statistical learning Geostatistics Weibull distribution Random forest 

Notes

Acknowledgments

The authors would like to thank the UK Meteorological Office for providing the wind speed data for this research and some of the covariates. Other data providers we would like to thank are: NASA for the Aster DTM and the EU for the land-cover raster.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Fabio Veronesi
    • 1
  • Stefano Grassi
    • 1
  • Martin Raubal
    • 1
  • Lorenz Hurni
    • 1
  1. 1.Institute of Cartography and GeoinformationZurichSwitzerland

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