Retraining surrogate models in increasingly restricted design spaces: a novel building energy model calibration method

Abstract

Surrogate (i.e. meta) models can approximate building energy models (BEMs) accurately and quickly, hence they have been widely used in BEM calibration studies. Typically, the surrogate models are trained a single time over the entire unknown building parameter space with a design such as Latin hypercube sampling. In this article, a multiple polynomial regression surrogate model is, instead, retrained with increasingly restricted designs. In each training repetition, the bounds of the design narrow around the unknown building parameter values that minimize the error between the surrogate model’s predictions and the measured energy. This ‘cascading surrogate’ calibration method finds CVRMSE values that are much lower than those of a powerful black box optimizer in a case study with simulated ‘measured’ data. However, the method has similar performance to the black box optimizer in a case study with real hourly measured energy, probably since the BEM was not configured accurately enough.

KEYWORDS:

• Building energy model
• calibration
• surrogate
• meta-model
• polynomial regression
• design of experiment

Acknowledgments

We would like to thank the National Science and Engineering Research Council of Canada, the Fonds de recherche du Québec Nature et Technologie, the Arbour foundation, the Fondation et alumni de Polytechnique Montréal, and Hydro-Québec for providing funding in the form of scholarships to the first author.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Future work

To provide further evidence of the cascading calibration method’s performance, it should be validated with other buildings and on other measured data, such as whole-building electricity consumption. Furthermore, the hyperparameters of the cascading calibration method should be tuned with real measured data, as opposed to simulated data, to see if it improves the method’s performance.

Data availability

Data will be made available on request.