Ongoing commissioning based on calibrated building energy models is one of the most promising means to improve the energy performance of existing buildings. Many calibration methods in the literature relied on whole-building utility data to calibrate building energy models. Recent studies revealed that only using this data could result in offsetting errors occurring at sub-utility levels. To reduce offsetting errors, a new bottom-up calibration method was developed where the zone, system, plant, and whole-building models are sequentially calibrated.

The number of candidate measurement points required for bottom-up calibration is large. Fortunately, building automation systems (BASs), common in many commercial/institutional buildings, can provide some of the required data. To reduce the time for BAS trend data analysis, a new proof-of-concept prototype, called the Automatic Assisted Calibration Tool (AACT) was developed and tested to couple trend data with calibrated simulation by automatically generating inputs to update an eQUEST input file.

This thesis documents the use of the AACT and bottom-up method to calibrate an eQUEST energy model of a case study building focusing on the zone and system level models. Using inputs generated from trend data and calibrating the zone level first often yielded a calibrated system level model. Limitations representing measured physical performance in eQUEST were encountered causing unintended offsetting errors occurring at the sub-utility levels. Overall, the use of BAS trend data with a bottom-up method can reduce offsetting errors during calibrated simulation.