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Building performance simulation can be used for retrofit analysis. However, it is time-consuming to create building energy models for existing buildings. This paper presented and implemented a rapid building energy modeling method for existing buildings by using prototype models and automatic model calibration for retrofit analysis with uncertainty. A shopping mall building located in Changsha, China, was selected as a case study to demonstrate the rapid modeling method. First, a toolkit named AutoBPS-Param was developed to generate building energy models with parameterized geometry data. A baseline EnergyPlus model was generated based on the building's basic information, including vintage, climate zone, total floor area, and percentage of each function type. Next, Monte Carlo sampling was applied to generate 1000 combinations for fourteen parameters. One thousand EnergyPlus models were created by modifying the baseline model with each parameter combination. Moreover, the 1000 simulation results were compared with the measured monthly electricity and natural gas usage data to find 29 calibrated solutions. Finally, the 29 calibrated energy models were used to evaluate the energy-saving potential of three energy conservation measures with uncertainty. The retrofit analysis results indicated that the electrical energy saving percentage of chiller replacement ranged from 1.57% to 13.51%, with an average of 8.27%. The energy-saving rate of lighting system replacement ranged from 1.92% to 11.66%, with an average of 6.43%. The energy-saving rate of window replacement ranges from 0.31% to 1.81%, with an average of 0.55%. The results showed that AutoBPS-Param could rapidly create building energy models for existing buildings and can be used for retrofit analysis after model calibration. [ABSTRACT FROM AUTHOR]

• A new urban building energy modeling tool-AutoBPS was developed. • AutoBPS calculated urban building energy demands, analyzed energy retrofit and rooftop PV potential via EnergyPlus. • AutoBPS automatically generated multi-zone models, considering shading from surrounding buildings. • A downtown district of 3633 buildings in Changsha, China, was simulated to demonstrate the tool's features. • All three energy conservation measures and PV installation could save 38.6% of total energy demand. China has promised to peak carbon emissions by 2030 and become carbon neutral before 2060. Achieving this target requires actions from all sectors, especially the building sector, which accounts for 33 % of the total energy consumption in China. Within this scenario, this paper introduces a new urban building energy modeling tool, Automated Building Performance Simulation (AutoBPS), to calculate urban building energy demands and to analyze energy retrofit and rooftop photovoltaic (PV) potential. AutoBPS uses a building-by-building approach to automatically generate multi-zone (one zone per floor) models based on the city building dataset, considering the shading effect by surrounding buildings. The tool simulates building energy use and PV generation via EnergyPlus. A downtown district of 3633 buildings in Changsha, China, was selected as a case study to demonstrate the features of the tool. Firstly, annual baseline energy uses of 1793 GWh were simulated by inputting Geographic Information System (GIS) data. Then, different energy conservation measures (covering envelope, lighting, cooling system upgrade, etc.) and roof PV generation systems were adopted to explore energy saving potential. The results showed that the window upgrade was the most energy-efficient individual measure for residential buildings, while the lighting upgrade was that one for commercial buildings. Altogether three measures could save 2.7 ∼ 30.7 % of annual site energy use intensity per building. The total energy demand could be reduced by 18.5 % by combining all measures and saved by 38.6 % when PV installation was added. AutoBPS can be easily applied to other cities, supporting urban energy efficiency plans for carbon reduction goal implementation. [ABSTRACT FROM AUTHOR]

• A dynamic setting temperature that adapts to climate change was proposed. • The method was based on clothing adjustment behavior which adapts to thermal comfort. • The dynamic setting temperature had significant energy saving potential. • The dynamic setting temperature is useful for intelligent control of air-conditioner. Saving energy consumption of air-conditioning is essential to building energy conservation while satisfying the thermal comfort requirements of indoor environment. On the basis of human clothing adjustment behavior, a dynamic setting temperature (DST) that adapts to climate change was proposed in this paper, i.e. indoor air-conditioning setting temperature changes dynamically with outdoor air temperature; furthermore. A method was established to obtain the DST. The daily mean clothing insulation was predicted according to the historical temperature at first, then indoor thermal comfort temperature was calculated based on the variable clothing insulation, and finally the DST was determined. According to the value or range of predicted mean vote (0, -0.5~0.5, -1~+1) corresponding to the thermal comfort, three levels of DST were defined. The corresponding thermal acceptability rates were 95%, 90% and 73%, respectively. A simulation of DST was conducted in a public building in Changsha, China based on meteorological data of 2017. Three levels of DST ranges were obtained, varied from 20.1 to 27.8 °C in level I, 17.8 to 28.7 °C in level II and 15.4 to 29.7 °C in level III, respectively. The simulation results of operational air-conditioning energy consumption shows that the application of DST could save up to 65.5% of energy consumption compared with the traditional fixed setting temperature (FST), and the operational energy consumption decreased by 59.1% due to the application of level-III DST compared with level-I DST. Therefore, the DST proposed in this paper has significant energy saving potential while meeting the thermal comfort requirements. [ABSTRACT FROM AUTHOR]