Given　the　nonlinear　power　load,　diversity　of　prediction　conditions,　subjectivity　of　parameter　setting　of　the　prediction　model,　etc.,　an　ISSA-GRU　(ISGU)　hybrid　model　based　on　the　combination　of　a　date　mapping　method　based　on　daily　average　load　with　strong　adaptability,　a　Gate　Recurrent　Unit　(GRU)　with　high　non-linear　fitting　performance　and　an　improved　sparrow　search　algorithm　(ISSA)　with　strong　search　ability　are　proposed　for　short-term　load　forecasting　(STLF).　First,　the　data　mapping　method　based　on　daily　average　load　is　used　to　map　the　week-holiday　factor　to　solve　the　problem　that　it　is　difficult　to　input　the　prediction　network　because　of　non-digitization.　Then,　highly　relevant　eigenvalues　are　selected　from　many　correlated　factors　to　deal　with　the　diversity　of　prediction　conditions.　Finally,　the　GRU　network　is　constructed　for　load　forecasting,　and　the　ISSA　algorithm　is　used　to　configure　GRU　network　parameters　objectively.　To　verify　the　effectiveness　of　the　ISGU　hybrid　model,　we　use　the　Singapore　power　load　data　experiment,　and　compare　the　experimental　results　with　the　existing　algorithms.　The　results　show　that　this　method　has　good　performance　for　STLF　and　effectively　improves　the　accuracy　of　STLF　statistical　standards.　©　2022　Power　System　Protection　and　Control　Press.　All　rights　reserved.