Evaluation of “Autotune” calibration against manual calibration of building energy models

• Background of “Autotune” automatic calibration for building energy models.
• Case Study 1 – Autotune recovery of original inputs from a manually de-tuned model.
• Best result achieved CV(RMSE) = 1.68% and MBE = 0.42% in less than 2 h.
• Over 50% of the 63 tunable parameters recovered within 10% of their original value.
• Case Study 2 – Autotune yields similar results to expert calibrator in less time.

This paper demonstrates the application of Autotune, a methodology aimed at automatically producing calibrated building energy models using measured data, in two case studies. In the first case, a building model is de-tuned by deliberately injecting faults into more than 60 parameters. This model was then calibrated using Autotune and its accuracy with respect to the original model was evaluated in terms of the industry-standard normalized mean bias error and coefficient of variation of root mean squared error metrics set forth in ASHRAE Guideline 14. In addition to whole-building energy consumption, outputs including lighting, plug load profiles, HVAC energy consumption, zone temperatures, and other variables were analyzed. In the second case, Autotune calibration is compared directly to experts’ manual calibration of an emulated-occupancy, full-size residential building with comparable calibration results in much less time. The paper concludes with a discussion of the key strengths and weaknesses of auto-calibration approaches.