Building　heating,　ventilation,　and　air　conditioning　(HVAC)　systems　consume　large　amounts　of　energy,　and　precise　energy　prediction　is　necessary　for　developing　various　energy-efficiency　strategies.　Energy　prediction　using　data-driven　models　has　received　increasing　attention　in　recent　years.　Typically,　two　types　of　driven　models　are　used　for　building　energy　prediction:　sequential　and　parallel　predictive　models.　The　latter　uses　the　historical　energy　of　the　target　building　as　training　data　to　predict　future　energy　consumption.　However,　for　newly　built　buildings　or　buildings　without　historical　data　records,　the　energy　can　be　estimated　using　the　parallel　model,　which　employs　the　energy　data　of　similar　buildings　as　training　data.　The　second　predictive　model　is　seldom　studied　because　the　model　input　feature　is　difficult　to　identify　and　collect.　Herein,　we　propose　a　novel　key-variable-based　parallel　HVAC　energy　predictive　model.　This　model　has　informative　input　features　(including　meteorological　data,　occupancy　activity,　and　key　variables　representing　building　and　system　characteristics)　and　a　simple　architecture.　A　general　key-variable　screening　toolkit　which　was　more　versatile　and　flexible　than　present　parametric　analysis　tools　was　developed　to　facilitate　the　selection　of　key　variables　for　the　parallel　HVAC　energy　predictive　model.　A　case　study　is　conducted　to　screen　the　key　variables　of　hotel　buildings　in　eastern　China,　based　on　which　a　parallel　chiller　energy　predictive　model　is　trained　and　tested.　The　average　cross-test　error　measured　in　terms　of　the　coefficient　of　variation　of　the　root　mean　square　error　(CV-RMSE)　and　normalized　mean　bias　error　(NMBE)　of　the　parallel　chiller　energy　predictive　model　is　approximately　16%　and　8.3%,　which　is　acceptable　for　energy　prediction　without　using　historical　energy　data　of　the　target　building.