Revealing the Impact of Climate Variability on the Wind Resource Using Data Mining Techniques (Poster)

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A data mining technique called 'k-means clustering' can be used to group winds at the NWTC into 4 major clusters. The frequency of some winds in the clusters is correlated with regional pressure gradients and climate indices. The technique could also be applied to wind resource assessment and selecting scenarios for flow modeling.

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1 pg.

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Clifton, A. & Lundquist, J. December 1, 2011.

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Description

A data mining technique called 'k-means clustering' can be used to group winds at the NWTC into 4 major clusters. The frequency of some winds in the clusters is correlated with regional pressure gradients and climate indices. The technique could also be applied to wind resource assessment and selecting scenarios for flow modeling.

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1 pg.

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  • Presented at the AGU Fall Meeting 2011, 5-9 December 2011, San Francisco, California; Related Information: NREL (National Renewable Energy Laboratory)

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  • Report No.: NREL/PO-5000-53526
  • Grant Number: AC36-08GO28308
  • Office of Scientific & Technical Information Report Number: 1031405
  • Archival Resource Key: ark:/67531/metadc833095

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  • December 1, 2011

Added to The UNT Digital Library

  • May 19, 2016, 3:16 p.m.

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  • Sept. 1, 2016, 1:18 p.m.

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Clifton, A. & Lundquist, J. Revealing the Impact of Climate Variability on the Wind Resource Using Data Mining Techniques (Poster), article, December 1, 2011; Golden, Colorado. (digital.library.unt.edu/ark:/67531/metadc833095/: accessed June 17, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.