Accurate　electric　load　forecasting　has　become　the　most　important　issue　in　energy　management;　however,　electric　load　demonstrates　a　seasonal/cyclic　tendency　from　economic　activities　or　the　cyclic　nature　of　climate.　The　applications　of　the　support　vector　regression　(SVR)　model　to　deal　with　seasonal/cyclic　electric　load　forecasting　have　not　been　widely　explored.　The　purpose　of　this　paper　is　to　present　a　SVR　model　which　combines　the　seasonal　adjustment　mechanism　and　a　chaotic　immune　algorithm　(namely　SSVRCIA)　to　forecast　monthly　electric　loads.　Based　on　the　operation　procedure　of　the　immune　algorithm　(IA),　if　the　population　diversity　of　an　initial　population　cannot　be　maintained　under　selective　pressure,　then　IA　could　only　seek　for　the　solutions　in　the　narrow　space　and　the　solution　is　far　from　the　global　optimum　(premature　convergence).　The　proposed　chaotic　immune　algorithm　(CIA)　based　on　the　chaos　optimization　algorithm　and　IA,　which　diversifies　the　initial　definition　domain　in　stochastic　optimization　procedures,　is　used　to　overcome　the　premature　local　optimum　issue　in　determining　three　parameters　of　a　SVR　model.　A　numerical　example　from　an　existing　reference　is　used　to　elucidate　the　forecasting　performance　of　the　proposed　SSVRCIA　model.　The　forecasting　results　indicate　that　the　proposed　model　yields　more　accurate　forecasting　results　than　the　ARIMA　and　TF-epsilon-SVR-SA　models,　and　therefore　the　SSVRCIA　model　is　a　promising　alternative　for　electric　load　forecasting.