LEADER 01851nam  2200433I  450 
001 9910703943403321
005 20140318144227.0
035   $a(CKB)5470000002446913
035   $a(OCoLC)873843414
035   $a(EXLCZ)995470000002446913
100   $a20140318d2013      ua             0
101 0 $aeng
135   $aurcn|||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aAssessing long-term wind conditions by combining different measure-correlate-predict algorithms $epreprint /$fJ. Zhang [and three others]
210  1$aGolden, Colorado :$cNational Renewable Energy Laboratory,$d2013.
215   $a1 online resource (11 pages) $ccolor illustrations
225 1 $aNREL/CP ;$v5500-57647
300   $aTitle from title screen (viewed on Mar. 18, 2014).
300   $a"August 2013."
300   $a"To be presented at the ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference Portland, Oregon, August 4-7, 2013."
320   $aIncludes bibliographical references (pages 10-11).
517   $aAssessing long-term wind conditions by combining different measure-correlate-predict algorithms 
606   $aWind power$xSimulation methods
606   $aWind power plants$xLocation$xMathematical models
606   $aWinds$xSpeed$xMeasurement
615  0$aWind power$xSimulation methods.
615  0$aWind power plants$xLocation$xMathematical models.
615  0$aWinds$xSpeed$xMeasurement.
700   $aZhang$b J.$01393143
712 02$aNational Renewable Energy Laboratory (U.S.),
801  0$bGPO
801  1$bGPO
906   $aBOOK
912   $a9910703943403321
996   $aAssessing long-term wind conditions by combining different measure-correlate-predict algorithms$93476119
997   $aUNINA