The　foundation　for　keeping　a　balance　between　power　supply　and　demand　is　power　load　forecasting.　The　power　load　is　crucial　data　for　substations　and　power　plants　to　plan　daily　power　generation　and　choose　the　power　system＇s　mode　of　operation.　It　is　crucial　to　increase　the　power　load　prediction　accuracy　in　order　to　better　plan　the　schedule　and　ensure　the　smooth　operation　of　the　power　grid　while　enhancing　system　efficiency.　To　address　the　non-linear　and　periodic　nature　of　the　time-series　changes　in　power　load　data,　a　hybrid　model　power　load　forecasting　approach　using　a　whale　algorithm　to　optimize　a　bi-directional　long　and　short-term　memory　network　(WOA-BiLSTM-Attention)　is　presented.　It　initially　fed　a　lot　of　historical　electricity　load　data.　The　WOA　portion　moves　in　accordance　with　the　fitness　value　to　update　the　population　and　arrive　at　the　global　optimal　solution　after　the　data　is　trained　and　predicted　by　the　network.　The　mean　squared　error　between　the　actual　output　value　and　the　desired　output　value　is　calculated.　Some　of　the　data　sets　from　the　10th　Teddy　Cup　Data　Mining　Challenge　B　were　chosen　for　prediction　in　this　work　(https://aistudio.baidu.com/aistudio/datasetdetail/140138/0).　The　experimental　results　demonstrate　that　the　suggested　method＇s　prediction　accuracy　is　superior　to　that　of　the　traditional　LSTM　network,　the　BiLSTM　network,　and　the　BP　network　and　that　the　R2　value　can　reach　0.9743.　©　2023　IEEE.