LEADER 02140nam  2200505   450 
001 9910713886703321
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100   $a20200824d2020      ua             0
101 0 $aeng
135   $aur|||||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aIdentification of worst impact zones for power grids during extreme weather events using Q-learning $epreprint /$fShuva Paul and Fei Ding
210  1$aGolden, CO :$cNational Renewable Energy Laboratory,$d2020.
215   $a1 online resource (5 pages) $cillustrations (some color)
225 1 $aConference paper ;$vNREL/CP-5D00-74737
300   $a"February 2020."
300   $a"Presented at the 2020 IEEE Conference on Innovative Smart Grid Technologies (IEEE ISGT), Washington, D.C., February 17-20 2020"--Page 1 of cover.
320   $aIncludes bibliographical references (page 5).
517   $aIdentification of worst impact zones for power grids during extreme weather events using Q-learning 
606   $aElectric power distribution$zUnited States$xComputer simulation
606   $aEmergency management$zUnited States
606   $aNatural disasters$zUnited States
606   $aInfrastructure (Economics)$zUnited States
606   $aElectric power failures$zUnited States$xComputer simulation
606   $aReinforcement learning$zUnited States
615  0$aElectric power distribution$xComputer simulation.
615  0$aEmergency management
615  0$aNatural disasters
615  0$aInfrastructure (Economics)
615  0$aElectric power failures$xComputer simulation.
615  0$aReinforcement learning
700   $aPaul$b Shuva$01410153
702   $aDing$b Fei
712 02$aNational Renewable Energy Laboratory (U.S.),
801  0$bGPO
801  1$bGPO
906   $aBOOK
912   $a9910713886703321
996   $aIdentification of worst impact zones for power grids during extreme weather events using Q-learning$93534625
997   $aUNINA