The　ever-increasing　deployment　of　cloud　data　centers　causes　high　energy　consumption,　high　cost,　and　harmful　environmental　pollution.　To　solve　above　problems,　cloud　service　providers　are　actively　exploring　to　use　green　cloud　data　centers　(GCDCs)　by　using　green　energy.　Yet　it　is　challenging　to　accurately　predict　the　future　wind　and　solar　energy　before　making　intelligent　task　scheduling　decisions.　In　addition,　it　is　difficult　to　jointly　optimize　cost　and　revenue.　In　this　work,　to　make　optimal　task　scheduling,　various　types　of　applications,　service　level　agreements,　service　rates,　task　loss　probability,　electricity　prices　and　green　energy　in　different　GCDCs　are　considered.　First,　this　work　employs　a　long　short-term　memory　network　to　predict　wind　and　solar　energy.　Then,　it　adopts　a　bi-objective　optimization　algorithm　to　achieve　a　better　trade-off　between　cost　and　revenue　of　GCDCs.　Finally,　it　adopts　real-world　data　including　workload　trace,　wind　energy,　solar　energy　and　electricity　prices　to　demonstrate　the　effectiveness　of　the　proposed　energy　prediction　and　task　scheduling　methods.　It＇s　shown　that　the　proposed　methods　achieve　higher　performance　than　other　neural　network　methods.　©　2020　IEEE.