Accurate　wind　power　prediction　can　alleviate　the　negative　influence　on　power　system　caused　by　the　integration　of　wind　farms　into　grid.　In　this　paper,　a　novel　combination　model　is　proposed　with　the　purpose　of　enhancing　short-term　wind　power　prediction　precision.　Singular　spectrum　analysis　is　utilized　to　decompose　the　original　wind　power　series　into　the　trend　component　and　the　fluctuation　component.　Then　least　squares　supportvector　machine　is　applied　to　forecast　the　trend　component　while　deep　belief　network　is　utilized　to　predict　the　fluctuation　component.　By　this　means,　the　advantages　of　the　above　two　forecasting　models　can　be　brought　into　full　play.　Moreover,　the　locality-sensitive　hashing　search　algorithm　is　introduced　to　cluster　the　nearest　training　samples　to　further　improve　forecasting　accuracy.　Besides,　the　effect　of　different　kernel　functions　on　the　performance　of　least　squares　supportvector　machine　is　investigated.　The　simulation　results　show　that　the　prediction　performance　of　the　proposed　combination　model　based　on　linear　kernel　function　outperforms　all　the　other　comparison　models　from　1-step　to　3-step　forecasting.　Therefore,　it　can　be　concluded　that　the　proposed　approach　provides　a　promising　and　effective　alternative　for　short-term　wind　power　prediction.　(C)　2018　Elsevier　Ltd.　All　rights　reserved.