Building　load　prediction　plays　an　important　role　in　building　energy　savings　and　mechanical　and　electrical　system　optimization　control.　The　dynamic　energy　consumption　can　be　accurately　calculated　using　the　traditional　physical　energy　simulation　method　that　entails　a　complex　setup　and　verification　process　due　to　the　numerous　input　parameters　required.　It　is　also　difficult　to　change　the　physical　model　once　it　has　been　determined.　The　datamining　method　is　fast　in　calculation　and　simple　to　use,　but　its　prediction　accuracy　is　limited　by　historical　data　quality.　It　is　difficult　to　predict　the　load　of　new　buildings　without　historical　data.　To　solve　these　problems,　this　study　proposes　a　hybrid　building　load　prediction　method　for　office　buildings.　The　proposed　method　uses　EnergyPlus　to　generate　a　building　load　database　that　includes　than　25.14　million　data　cases,　covering　35　types　of　building　geometry　and　2870　building　examples.　Based　on　the　above　database,　the　LightGBM　algorithm　was　selected　to　extract　feature　variables　that　affect　the　load　and　build　a　load　prediction　model.　The　training　results　show　that　there　are　24　key　feature　variables　for　office　building　load　prediction.　The　hourly　MAPE　of　the　cooling　load　prediction　model　is　6.95　%　and　RMSE　is　4.31　W/m2,　and　the　hourly　MAPE　of　the　heating　load　prediction　model　is　7.09　%　and　RMSE　is　11.64　W/m2　compared　with　EnergyPlus　model.　Two　actual　office　buildings　are　selected　as　case　studies　to　validate　the　model　prediction　accuracy.　Results　show　that　comparing　predicted　results　with　measured　data,　the　hourly　cooling　load　of　the　MAPE　is　12.42　%.　Coparing　predicted　results　with　actual　heating　load,　daily　MAPE　is　7.97　%.