This paper proposed and implemented a novel method to rapidly generate building energy modeling for existing buildings with measured energy data by integrating the prototype building energy model and automatic model calibration. The generated models were applied for retrofit analysis with uncertainty. First, a prototype model for shopping mall buildings was proposed to generate a baseline EnergyPlus model based on the building’s basic information, including vintage, climate zone, total floor area, and percentage of each function type. Next, an automatic calibration algorithm was implemented to calibrate the baseline model based on the monthly electricity and natural gas usage data. Monte Carlo sampling was applied to generate 1000 combinations for fourteen parameters. Multiple solutions that meet the calibration criteria can be found. Moreover, the calibrated energy models were used to evaluate the energy-saving potential of several energy conservation measures. 29 EnergyPlus models that meet the calibration criteria are found. The lighting power density in those 29 models ranges from 11.4 to 14.9 W/m2 with an average of 13.1 W/m2; while the chiller COP ranges from 3.45 to 4.79 with an average of 4.00. The electricity energy saving percentage of replacing lights with LED lights ranges from 1.9% to 11.7% with an average of 6.1%; while the electricity energy saving percentage of chiller replacement ranges from 1.6% to 14.1% with an average of 8.4%. The results show a high level of uncertainty when the actual lighting power density and chiller cop information is unknown.
Keywords AutoBPS, shopping mall, model calibration, EnergyPlus, Monte Carlo