Using　professional　software,　the　simulation　method　can　accurately　calculate　the　dynamic　energy　consumption,　however,　the　input　parameters　are　cumbersome　and　often　cannot　be　changed　after　the　building　geometric　model　is　determined.　Data　mining　method　is　fast　and　can　be　applied　in　various　conditions,　however,　it　needs　long-time　historical　training　data　and　is　greatly　affected　by　data　quality.　Based　on　the　above　characteristics,　an　energy　consumption　prediction　model　of　office　buildings　was　proposed　based　on　the　variables　extraction　from　large　simulation　examples　in　this　paper.　EnergyPlus　was　used　to　build　bulk　models　of　typical　office　buildings　and　adjust　input　parameters　to　generate　a　database　of　millions　of　data.　LightGBM　algorithm　was　used　to　screen　the　characteristic　factors　affecting　the　load　and　construct　the　load　forecasting　model.　Results　show　that　the　selected　24　dimensional　characteristic　variables　can　ensure　that　the　prediction　accuracy　of　the　model　is　more　than　90%　.　Combined　with　the　energy　consumption　calculation　model　of　air-conditioning　equipment　in　EnergyPlus,　the　energy　consumption　prediction　was　achieved　by　python　compilation.　An　office　building　in　Beijing　was　selected　as　a　research　case.　The　average　relative　error　of　daily　energy　consumption　of　the　measurement　period　was　8.　27%　.　Then,　typical　annual　weather　data　was　used　to　calculate　annual　building　energy　consumption　and　a　monthly　average　relative　error　of　10.　37%　was　obtained.　The　predicted　energy　use　intensity　was　36.　25　kW·h　/　(m2·a),　while　the　real　value　was　35.　20　kW·h　/　(m2·a),　with　a　relative　error　of　2.　98%　.　©　2023　Beijing　University　of　Technology.　All　rights　reserved.