Calibrating a white-box building energy model (BEM) is time-consuming and prohibitively expensive due to the large number of required model inputs and the limited availability of measurements. Therefore, this research aims to increase the calibration efficiency by proposing an improved workflow to obtain a quick, lightweight, and parsimonious model that only requires building automation system (BAS) and energy meter data and simple geometric drawings. The proposed method was demonstrated with a case study building in Ottawa, Canada. The results indicated more accurate parameters estimates and significantly more reliable energy consumption projections when the model is calibrated using energy meter data at a higher temporal resolution. Applying control interventions to calibrated BEM showed that up to 34% of energy could be saved through the optimized operation. Furthermore, leveraging BAS data not only overcame the overparameterization issue but was also found useful to detect operational anomalies to support optimized operation.