The　correlations　among　the　building　energy　consumption　factors　can　corrupt　the　prediction　model＇s　performance,　and　get　undesirable　results.　A　prediction　model　based　on　KPCA-WLSSVM　is　proposed　to　forecast　building　energy　consumption.　The　kernel　principal　component　analysis　(KPCA)　method　could　not　only　solve　the　linear　correlation　of　the　input　and　compress　data　but　also　simplify　the　model　structure.　A　novel　hybrid　chaos　particle　swarm　optimization　simulated　annealing　(CPSO-SA)　algorithm　is　applied　to　optimize　WLSSVM　parameters　to　improve　learning　performance　and　generalization　ability　of　the　model.　Furthermore,　the　KPCA-WLSSVM　model　is　applied　to　the　energy　consumption　prediction　for　an　office　building,　and　the　results　show　that　the　KPCA-WLSSVM　has　better　accuracy　compared　with　WLSSVM　model,　LSSVM　model　and　RBF　neural　network　model.　and　the　KPCA-WLSSVM　is　effective　for　building　energy　consumption　prediction.　©　2015,　China　National　Publications　Import　and　Export　(Group)　Corporation.　All　right　reserved.