Recently,　＂power　cuts＂　and　＂coal　price　surges＂　have　been　significant　concerns　of　individuals　and　societies.　The　main　reasons　for　a　power　cut　are　a　recent　rapid　increase　in　power　consumption,　shortage　of　thermal　coal　or　the　large　shutdown　capacity　of　thermal　power　units,　resulting　in　a　tight　power　supply　in　the　power　grid.　In　recent　years,　the　shortage　of　fossil　resources　has　led　to　frequent　energy　crises.　In　the　context　of　carbon　peaks　and　carbon　neutralization,　how　to　better　develop　electric-energy　substitution　and　eliminate　the　dependence　on　fossil　energy　has　become　a　problem　that　needs　to　be　solved　at　present.　In　this　paper,　the　influencing　factors　of　electric-energy　substitution　in　Beijing　are　analyzed,　and　the　indexes　affecting　the　electric-energy　substitution　are　outlined.　By　constructing　various　machine-learning　models,　the　prediction　is　performed.　The　results　show　that　the　Gaussian　kernel　support　vector　machine　model　based　on　a　grid　search　has　a　good　prediction　effect　on　the　electric-energy　substitution　potential　in　Beijing,　which　has　certain　guiding　significance　for　electric-energy　substitution　potential　analysis.