When　the　historical　renewable　power　generation　data　are　missing,　the　data-driven　scenario　generation　methods　may　be　invalid.　To　deal　with　this　issue,　in　this　paper,　we　propose　a　conditional　deep　convolutions　generative　adversarial　network　(C-DCGAN)　to　recover　and　transfer　the　historical　renewable　power　generation　data.　First,　a　renewable　power　plant　with　a　lot　of　missing　historical　data　is　considered　as　the　target　plant,　and　a　neighboring　plant　with　sufficient　historical　data　is　considered　as　the　source　plant.　Then,　the　proposed　C-DCGAN　model　learns　the　correlation　between　the　data　in　the　target　plant　and　the　source　plant.　After　that,　the　C-DCGAN　model　recovers　the　missing　data　in　the　target　plant　based　on　the　historical　data　in　the　source　plant.　In　this　way,　the　historical　data　in　the　source　plant　is　transferred　to　the　target　plant.　Finally,　the　numerical　experiments　have　been　carried　out　based　on　a　wind　farm　data,　and　some　statistical　indicators　are　used　to　evaluate　the　data　recovered　by　the　proposed　C-DCGAN　model　and　the　conditional　generative　adversarial　network　(CGAN)　model.　The　simulation　results　show　that　the　proposed　C-DCGAN　model　has　better　performance　in　renewable　power　generation　data　transferring　compared　with　the　CGAN　model.　©　2022,　Power　System　Technology　Press.　All　right　reserved.