Calibrated Building Energy Modeling (CBEM) was applied to determine monthly energy bill allocations for a non-metered residential community. The calibration was performed using community level utility bills, real weather data, building survey data, and occupant behavior. The procedures can be applied to other buildings and occupant profiles. The results can be translated into algorithms in the form of an easy to use computer tool. Additionally, the monthly utility bill allotments can be calculated each month based on real weather for tenant review and instant behavior education.

Accurately predicting building energy usage is of great importance in various efforts on improving building energy efficiency. Data-driven approach and first-principle approach are commonly used in developing models for building energy usage prediction. We employed different data driven methods to predict hourly energy usages in two buildings. One was a synthetic large-size office building from DOE reference building models. The other was an existing commercial building. The obtained hourly energy consumption for each building was divided into training and testing sets. We focused on evaluating the accuracy and model complexity of data-driven models for predicting energy usage of two buildings.

For a new LEED Platinum building, a forward model was integrated in the building design process and offered the flexibility to evaluate different design options and compare overall energy efficiency to the local building energy-efficiency code. The model was calibrated using 1.5 years' of measured building performance data after occupancy in 2012. The calibrated model energy use outputs are now being used to compare with the latest two years' (2014, 2015) of measured building energy data. In this session, the comparison results are presented. The effectiveness of using calibrated model output as a benchmark for building energy savings calculation is analyzed.