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100   $a20200803d2019      ua             0
101 0 $aeng
135   $aur|||||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aData-driven load diversity and variability modeling for quasi-static time-series simulation on distribution feeders $epreprint /$fXiangqi Zhu and Barry Mather
210  1$aGolden, CO :$cNational Renewable Energy Laboratory,$d2019.
215   $a1 online resource (5 pages) $ccolor illustrations
225 1 $aNREL/CP ;$v5D00-73146
300   $aPresented at the 2019 IEEE Power and Energy Society General Meeting (IEEE PES GM), 4-8 August 2019, Atlanta, Georgia.
300   $a"October 2019."
320   $aIncludes bibliographical references.
517   $aData-driven load diversity and variability modeling for quasi-static time-series simulation on distribution feeders 
606   $aElectric power distribution
606   $aTime-series analysis
615  0$aElectric power distribution.
615  0$aTime-series analysis.
700   $aZhu$b Xiangqi$01392247
712 02$aNational Renewable Energy Laboratory (U.S.),
801  0$bGPO
801  1$bGPO
906   $aBOOK
912   $a9910713743603321
996   $aData-driven load diversity and variability modeling for quasi-static time-series simulation on distribution feeders$93497313
997   $aUNINA