The　accuracy　level　of　short-term　load　forecasting　(STLF)　affects　the　power　department＇s　arrangements　for　unit　start-up,　shutdown,　overhaul,　and　load　dispatching.　However,　the　existing　algorithms　do　not　fully　consider　load　volatility　and　difficulty　in　setting　the　algorithm　parameters.　In　this　regard,　this　paper　proposes　a　hybrid　model　which　combines　complete　ensemble　empirical　mode　decomposition　with　adaptive　noise　(CEEMDAN),　gated　recurrent　unit　(GRU)　with　improved　gray　wolf　optimizer　(IGWO),　namely　CEEMDAN–IGWO–GRU　(CIG)　hybrid　algorithm.　Firstly,　the　details　and　trend　information　of　the　load　signal　are　separated　by　the　CEEMDAN　algorithm　to　suppress　the　interference　of　load　fluctuation.　Then,　the　GRU　network　optimized　by　IGWO　parameters　is　used　to　predict　each　component,　separately.　Finally,　the　complete　load　forecasting　results　are　obtained　by　reconstructing　the　forecasting　result　of　each　component.　The　power　load　data　of　a　certain　area　under　study　is　used　to　verify　the　CIG　model,　and　the　experimental　results　are　compared　with　other　existing　algorithms.　The　experimental　results　show　that　the　load　forecasting　results　of　the　CIG　model　get　the　high-precision　evaluation　of　0.6997%,　52.4685,　and　38.1891 MW　in　MAPE,　RMSE,　and　MAE,　respectively.　Therefore,　the　parameter　optimization　ability　of　the　IGWO　algorithm　can　effectively　improve　the　prediction　accuracy,　and　the　CIG　method　can　availably　suppress　the　impact　of　load　fluctuation　on　prediction　and　has　a　powerful　nonlinear　fitting　ability.　In　conclusion,　CIG　has　great　potential　in　establishing　a　power　load　forecasting　model.　©　2022,　The　Author(s),　under　exclusive　licence　to　Springer-Verlag　GmbH　Germany,　part　of　Springer　Nature.