Various　supervised　machine-learning　algorithms　for　wind　power　forecasting　have　been　developed　in　recent　years　to　manage　wind　power　fluctuations　and　effectively　correlate　to　energy　consumption;　Meanwhile,　the　performance　of　the　model　does　suffer　from　missing　values.　To　address　the　issue　of　missing　values　in　wind　power　forecast,　this　paper　proposes　two　methods:　Clue-based　missing　at　random　(CMAR)　and　patterned　k-nearest　Neighbor　(PkNN).　In　addition,　a　hybrid　wind　energy　forecasting　system　has　been　created　that　is　built　on　1D　-Convolutional　neural　networks,　which　are　used　to　extract　features　from　raw　input,　and　Long　Short-Term　Memory,　which　employs　time　series　data　internal　representation　learning　to　improve　the　accuracy　of　month-wise　wind　power　forecasting.　The　efficacy　of　the　proposed　model　on　generated　datasets　is　also　compared　to　the　classic　machine　learning　model　to　check　the　generalization　ability.　The　strength　of　the　Convolutional　LSTM　has　been　estimated　in　terms　of　different　performance　metrics　such　as　mean　absolute　error　(MAE),　root　mean　squared　error　(RMSE),　R2,　and　explained　variance　score　(EVS).　The　experimental　results　show　that　the　use　of　the　PkNN　algorithm　for　data　imputation;　integrated　with　regression-based　Convolutional　LSTM　is　much　more　efficient　in　prediction　over　other　deep　neural　network　models.