To　reduce　the　short-term　load　forecasting　(STLF)　error　of　off-line　forecasting　model,　a　VMD-IWOA-LSTM　(VIL)　method　for　STLF　is　proposed.　Firstly,　variational　mode　decomposition　(VMD)　is　used　to　decompose　the　historical　power　load　signals.　Then,　the　decomposed　signals　are　reconstructed　according　to　the　similarity　of　Pearson　correlation　coefficient　(PCC),　and　meteorological　data　are　chosen　for　each　reconstructed　component　based　on　the　set　PCC　threshold.　The　long　short-term　memory　(LSTM)　models　are　used　to　predict　the　corresponding　components,　and　improved　whale　optimization　algorithm　(IWOA)　is　used　to　optimize　the　parameters　in　LSTM.　Finally,　the　forecast　results　of　each　component　are　added　together　to　get　the　final　forecast　result.　The　experimental　results　of　power　load　data　in　a　certain　area　show　that　the　proposed　method　has　the　advantages　of　strong　anti-interference　performance　and　high　prediction　accuracy　compared　with　other　methods,　and　has　strong　practicability.　©　2022　Institute　of　Electrical　Engineers　of　Japan.　Published　by　Wiley　Periodicals　LLC.　©　2022　Institute　of　Electrical　Engineers　of　Japan.　Published　by　Wiley　Periodicals　LLC.