To　improve　the　accuracy　of　building　air-conditioning　load　prediction,　we　proposed　an　adaptive　weighted　least　squares　support　vector　machine　(AWLS-SVM)　algorithm　based　on　analyzing　the　main　related　factors.　This　algorithm　uses　least　squares　support　vector　machine　regression　to　develop　the　model　and　obtain　the　sample　data　fitting　error,　and　calculates　the　initial　weight　according　to　the　statistical　characteristics　of　the　prediction　errors.　The　particle　swarm　optimization　(PSO)　is　applied　to　obtain　the　optimal　parameters　of　the　AWLS-SVM,　so　as　to　improve　its　forecasting　accuracy.　Based　on　the　simulated　data　from　the　DeST　platform,　we　used　the　AWLS-SVM　model　to　predict　the　hourly　air-conditioning　load　of　an　office　building　in　South　China.　The　simulated　results　show　that　the　AWLS-SVM　has　better　accuracy　compared　with　RBFNN　model,　LS-SVM　model　and　WLS-SVM　model,　with　a　mean　absolute　error　reduced　by　51.84%,　13.95%　and　3.24%,　respectively.　We　also　used　the　AWLS-SVM　method　to　build　a　prediction　model　for　the　air-conditioning　load　of　another　office　building　based　on　real　measurements.　The　results　show　that　AWLS-SVM　method　outperform　the　other　three　models　in　terms　of　accumulated　error,　demonstrating　that　AWLS-SVM　is　effective　for　building　air-conditioning　load　prediction.　©　2016,　Chongqing　Medical　University.　All　right　reserved.