Scalable methodology for large scale building energy improvement: Relevance of calibration in model-based retrofit analysis

The increasing interest in retrofitting of existing buildings is motivated by the need to make a major contribution to enhancing building energy efficiency and reducing energy consumption and CO2 emission by the built environment. This paper examines the relevance of calibration in model-based analysis to support decision-making for energy and carbon efficiency retrofits of individual buildings and portfolios of buildings. The authors formulate a set of real retrofit decision-making situations and evaluate the role of calibration by using a case study that compares predictions and decisions from an uncalibrated model with those of a calibrated model. The case study illustrates both the mechanics and outcomes of a practical alternative to the expert- and time-intense application of dynamic energy simulation models for large-scale retrofit decision-making under uncertainty. •We present a retrofit analysis methodology to support large scale energy retrofit.•The methodology is based on Bayesian calibration and normative energy models.•We examine the relevance of calibration in retrofit decision-making situations.•Case studies demonstrate the value of the method for risk-conscious decision-making.