Abstract
•Sixty optimization-based calibration approaches for building models are compared.•An automated, genetic multi-objective optimization is developed in MATLAB.•A full-specified case study without uncertainty in model inputs and outputs is used.•The accuracy of the approaches in calibration variables prediction is presented.•CV(RMSE) is the most robust error function under different calibration datasets.
Building Energy Model (BEM) calibration is the process of reducing the gap between the simulation outputs and the actual measured data at the same conditions. The literature shows that BEM calibration approaches could lead to a significant error in the model inputs even the calibration has been conducted successfully based on the model outputs (i.e., error functions). This paper compares the performance (i.e., accuracy and robustness) of 60 optimization-based calibration approaches. The approaches have different error functions (individual or combination of NMBE, NME, CV(RMSE), R2 ,Cχ2) to be minimized and different outputs (heating demand, cooling demand, and\or indoor temperature for weeks, months, or a year) to be calibrated. The BESTEST600, predefined by ANSI/ASHRAE 140–2001, is selected as a white-box BEM case study for conducting the comparison test. Having the case study inputs and outputs without uncertainty gives a trustworthy comparison between the tested approaches. EnergyPlus is used for conducting the simulation while the Multi-objective optimization algorithm (a variant of NSGA-II) from MATLAB is used to minimize the error function(s) associated to each calibration approach. Among the 60 calibration approaches, eight proved to be the most accurate in predicting all calibration variables with percentage errors lower than 10%. CV(RMSE) was found to be the most robust error function under different calibration datasets. The results also show that the current standard calibration requirements are not proper as stopping criteria for automatic optimization-based calibration.