This　paper　proposes　a　hybrid　short-term　load　forecasting　method,　which　is　based　on　the　fuzzy　combination　weights　as　well　as　the　empirical　mode　decomposition　process　(FCW-EMD),　and　support　vector　machine　optimized　via　the　Bat　algorithm　as　well　as　the　Kalman　filtering　process　(KF-BA-SVM).　The　subjective　weight　is　presented　as　a　new　theory　and　is　applied　to　capture　the　inherent　correlation　effectively　among　hourly　loads.　Based　on　the　proposed　objective　weights　and　subjective　weights,　the　fuzzy　combination　weights　theory　(FCW)-a　new　similar　day　selection　theory　is　presented,　which　improves　the　accuracy　of　the　similar　day　selection,　and　correspondingly,　makes　the　original　data　for　EMD　processing　decrease　dramatically.　BA　is　introduced　to　optimize　parameters　of　the　SVM　model　for　further　improving　the　forecasting　accuracy.　Using　the　decomposed　load　series　via　empirical　model　decomposition　(EMD)　as　inputs　to　SVM　and　further　correcting　the　output　of　SVM　via　KF,　a　hybrid　FCW-EMD　and　KF-BA-SVM　short-term　load　forecasting　method　is　established.　Numerical　case　studies　on　the　load　forecasting　of　a　transformer　substation　in　south　China　show　that　the　proposed　hybrid　forecasting　model　outperforms　other　forecasting　methods　and　effectively　improves　the　prediction　accuracy.