Application　of　support　vector　regression　(SVR)　with　chaotic　sequence　and　evolutionary　algorithms　not　only　could　improve　forecasting　accuracy　performance,　but　also　could　effectively　avoid　converging　prematurely.　However,　the　tendency　of　electric　load　sometimes　reveals　cyclic　changes　due　to　seasonal　economic　activities　or　climate　seasonal　nature.　The　applications　of　SVR　model　to　deal　with　cyclic　electric　load　forecasting　have　not　been　widely　explored.　This　investigation　presents　a　SVR-based　electric　load　forecasting　model　which　applied　a　novel　hybrid　algorithm,　namely　chaotic　genetic　algorithm-simulated　annealing　algorithm　(CGASA),　to　improve　the　forecasting　performance.　In　addition,　seasonal　adjustment　mechanism　is　also　employed　to　deal　with　cyclic　electric　loading　tendency.　A　numerical　example　from　an　existed　reference　is　used　to　elucidate　the　forecasting　performance　of　the　proposed　seasonal　support　vector　regression　with　chaotic　genetic　algorithm,　namely　SSVRCGASA　model.　The　forecasting　results　indicate　that　the　proposed　model　yields　more　accurate　forecasting　results　than　ARIMA　and　TF-epsilon-SVR-SA　models　in　existed　papers.　Therefore,　the　SSVRCGASA　model　is　a　promising　alternative　for　electric　load　forecasting.