Using calibrated minimum input models to predict retrofit performance on residential dwellings

Abstract

Accurate energy models for existing buildings depend on the appropriate calibration of a large number of unknown parameters against detailed monitoring data. This process typically requires significant computational effort and often produces an underdetermined model. Simplifying the model by reducing unknown parameters can alleviate these challenges. This study assesses whether a previously introduced minimum input method, which uses a subset of significant parameters for simplified calibration, can predict post-retrofit performance. The paper compares the predictive power of this new method with conventional calibration, using post-retrofit monitored data from three social housing dwellings. Results indicate that minimum input models achieve significant improvements in retrofit prediction from uncalibrated models and can outperform conventionally calibrated models. On average, minimum input models reduced CVRMSE by 5.6% from baseline, slightly less than the 6.3% improvement from full calibration. Our results support simplified calibration as a viable alternative for predicting thermal performance from residential retrofits.