In　response　to　the　increasingly　severe　energy　consumption　problem　and　to　promote　energy　saving　and　emission　reduction,　this　study　aims　to　design　and　apply　an　energy　management　system　platform　based　on　artificial　intelligence　(AI)　technology.　The　system　adopts　sensor　technology　and　data　acquisition　equipment　to　monitor　various　types　of　energy　consumption　in　buildings　in　real　time,　efficiently　process　and　predict　these　data　through　machine　learning　algorithms,　and　finally　visualize　the　results.　The　system　is　functionally　complete,　completing　the　process　from　data　collection　to　visualization,　the　cloud　platform’s　construction,　and　finally　a　full　energy　management　platform.　Various　machine　learning　methods　are　applied　to　energy　management　by　predicting　the　chilled　water　energy　meter　return　temperature　of　the　central　air-conditioning　system　and　comparing　its　performance.　Among　the　various　types　of　regression　algorithms,　the　mean-square　error　(MSE)　of　decision　tree　regression　is　0.36,　the　MSE　of　support　vector　regression　(SVR)　is　0.09,　the　MSE　of　K-nearest　neighbor　(KNN)　regression　is　0.57,　and　the　MSE　of　extreme　gradient　boosting　(XGBoost)　regression　is　0.32.　The　SVR,　the　XGBoost　regression,　and　the　decision　tree　regression　perform　better　in　various　indices.　©　2025　by　the　authors.