Calibrated detailed energy models are often used to identify economic deep retrofit opportunities in existing buildings. But the uncertainty in building performance makes it technically unrealistic to reach a single “best fit” model without extensive sub-metering and domain experts on the project which is typically labor intensive. To address this problem, researchers have been adopting stochastic modeling as a more reliable approach to calibrate building energy models. A set of all plausible models is found, rather than a best fit model, which accounts for uncertainties in existing building properties and conditions. In addition, sensor data collected from within a building can be used to identify key operational characteristics such as setpoint temperatures, carbon-dioxide levels, light levels, and temperature setbacks. This paper presents a hybrid calibration methodology for building energy models using a combination of short-term wireless sensor data, 15-min interval smart meter data and stochastic modeling. The hybrid approach provides a means to calibrate the operational variables and physical variables separately, reducing potential bias and errors and to reach a set of plausible model solutions. A case study is presented to demonstrate the strength of the calibration methodology.
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