The　electricity　load　of　iron　and　steel　enterprises　is　greatly　affected　by　the　production　conditions　and　physical　process　parameters.　The　existing　research　ignores　the　coupling　effect　of　material　flow　and　energy　flow　between　processes,　resulting　in　insufficient　simulation　accuracy.　To　solve　the　above　problems,　this　paper　presents　a　short-process　power　model　of　steel　production　driven　by　mechanism　and　data.　Firstly,　based　on　the　coupling　characteristics　of　material　flow　and　energy　flow　in　the　transport　process,　the　attenuation　law　of　physical　quantities　such　as　mass　and　temperature　value　between　the　successive　processes　is　derived.　Secondly,　a　general　expression　of　the　power　function　is　established　according　to　the　operation　properties　of　the　equipment,　and　a　kernel　extreme　learning　machine　is　used　to　fit　the　physical　quantities　and　characteristic　parameters　of　the　power　function.　Finally,　the　power　of　different　processes　is　superimposed　in　time　domain,　and　the　total　power　curve　for　the　short-process　iron　and　steel　enterprise　is　obtained.　The　simulation　results　of　a　domestic　steelmaking　enterprise　show　that　the　proposed　model　can　reflect　power　characteristics　more　accurately.　©2025　Chin.Soc.for　Elec.Eng.