During　the　operation　of　HVAC　(Heating,　Ventilation,　and　Air-Conditioning)　systems,　precise　energy　consumption　prediction　plays　an　important　role　in　achieving　energy　savings　and　optimizing　system　performance.　However,　the　HVAC　system　is　a　complex　and　dynamic　system　characterized　by　a　large　number　of　variables　that　exhibit　significant　changes　over　time.　Therefore,　it　is　inadequate　to　rely　on　a　fixed　offline　model　to　adapt　to　the　dynamic　changes　in　the　system　that　consume　tremendous　computation　time.　To　solve　this　problem,　a　deep　neural　network　(DNN)　model　based　on　Just-in-Time　learning　with　hyperparameter　R　(RJITL)　is　proposed　in　this　paper　to　predict　HVAC　energy　consumption.　Firstly,　relevant　samples　are　selected　using　Euclidean　distance　weighted　by　Spearman　coefficients.　Subsequently,　local　models　are　constructed　using　deep　neural　networks　supplemented　with　optimization　techniques　to　enable　real-Time　rolling　energy　consumption　prediction.　Then,　the　ensemble　JITL　model　mitigates　the　influence　of　local　features,　and　improves　prediction　accuracy.　Finally,　the　local　models　can　be　adaptively　updated　to　reduce　the　training　time　of　the　overall　model　by　defining　the　update　rule　(hyperparameter　R)　for　the　JITL　model.　Experimental　results　on　energy　consumption　prediction　for　the　HVAC　system　show　that　the　proposed　DNN-RJITL　method　achieves　an　average　improvement　of　5.17%　in　accuracy　and　41.72%　in　speed　compared　to　traditional　methods.　©　2024-IOS　Press.　All　rights　reserved.