Current　large-scale　green　cloud　data　centers　(GCDCs)　tend　to　consume　a　huge　amount　of　energy　and　generate　enormous　carbon　emissions.　Existing　studies　have　tried　to　solve　this　problem　by　either　realizing　prediction　of　green　energy,　or　optimizing　task　scheduling.　In　contrast,　this　work　seamlessly　combines　green　energy　prediction　and　task　scheduling　to　jointly　optimize　revenue　and　energy　cost　of　GCDCs.　Specifically,　this　work　designs　a　prediction　method,　named　Savitzky-Golay　and　Long　Short-Term　Memory　network　(SG-LSTM),　to　realize　noise　filtering　and　forecast　green　energy.　Based　on　such　prediction,　a　bi-objective　optimization　method,　named　Decomposition-based　Multi-objective　evolutionary　algorithm　with　Gaussian　mutation　and　Crowding　distance　(DMGC),　is　developed　to　optimize　the　revenue　and　energy　cost　of　GCDCs.　Its　performance　is　demonstrated　over　real-life　datasets　including　Google　cluster　traces,　wind　speeds,　solar　irradiance　and　prices　of　electricity.　Experimental　results　show　that　SG-LSTM　outperforms　its　two　peers,　back　propagation　neural　network　and　gated　recurrent　unit,　in　terms　of　root　mean　square　errors　and　mean　absolute　errors.　In　addition,　DMGC　surpasses　its　such　peers　as　NSGA-II,　SPEA2,　and　MOEA/D　in　terms　of　revenue,　energy　cost　and　average　execution　time.　Particularly,　DMGC＇s　revenue　is　18%,　20%　and　13.1%　higher,　energy　cost　is　16%,　19.8%　and　15.2%　lower,　and　average　execution　time　is　60.02%,　38.47%　and　24.17%　lower　than　those　of　NSGA-II,　SPEA2,　and　MOEA/D,　respectively.