LEADER 01526oam  2200433   450 
001 9910704857703321
005 20140313142019.0
035   $a(CKB)5470000002445158
035   $a(OCoLC)868066476
035   $a(EXLCZ)995470000002445158
100   $a20140114d2013      ua             0
101 0 $aeng
135   $aurmn||||a||||
181   $2rdacontent
182   $2rdamedia
183   $2rdacarrier
200 10$aUsing machine learning to create turbine performance models /$fAndy Clifton
210  1$a[Golden, Colo.] :$cNational Renewable Energy Laboratory,$d[2013]
215   $a1 online resource (17 pages) $ccolor illustrations
225 1 $aNREL/PR ;$v5000-58314
300   $aTitle from title screen (viewed on Jan. 14, 2014).
300   $a"May 2013."
300   $a"Presented to Power Curve Working Group Meeting II, Brande, Denmark, March 12 2013."
320   $aIncludes bibliographical references.
606   $aTurbines$xTechnological innovations
606   $aMachine learning
606   $aInterconnected electric utility systems$zUnited States
615  0$aTurbines$xTechnological innovations.
615  0$aMachine learning.
615  0$aInterconnected electric utility systems
700   $aClifton$b Andy$01385955
712 02$aNational Renewable Energy Laboratory (U.S.),
801  0$bGPO
801  1$bGPO
801  2$bGPO
906   $aBOOK
912   $a9910704857703321
996   $aUsing machine learning to create turbine performance models$93533320
997   $aUNINA