Calibration of building energy models for retrofit analysis under uncertainty

Retrofitting existing buildings is urgent given the increasing need to improve the energy efficiency of the existing building stock. This paper presents a scalable, probabilistic methodology that can support large scale investments in energy retrofit of buildings while accounting for uncertainty. The methodology is based on Bayesian calibration of normative energy models. Based on CEN-ISO standards, normative energy models are light-weight, quasi-steady state formulations of heat balance equations, which makes them appropriate for modeling large sets of buildings efficiently. Calibration of these models enables improved representation of the actual buildings and quantification of uncertainties associated with model parameters. In addition, the calibrated models can incorporate additional uncertainties coming from retrofit interventions to generate probabilistic predictions of retrofit performance. Probabilistic outputs can be straightforwardly translated to quantify risks of under-performance associated with retrofit interventions. A case study demonstrates that the proposed methodology with the use of normative models can correctly evaluate energy retrofit options and support risk conscious decision-making by explicitly inspecting risks associated with each retrofit option.