Uncertainty and sensitivity decomposition of building energy models

Abstract

As building energy modelling becomes more sophisticated, the amount of user input and the number of parameters used to define the models continue to grow. There are numerous sources of uncertainty in these parameters, especially when the modelling process is being performed before construction and commissioning. Past efforts to perform sensitivity and uncertainty analysis have focused on tens of parameters, while in this work, we increase the size of analysis by two orders of magnitude (by studying the influence of about 1000 parameters). We extend traditional sensitivity analysis in order to decompose the pathway as uncertainty flows through the dynamics, which identifies which internal or intermediate processes transmit the most uncertainty to the final output. We present these results as a method that is applicable to many different modelling tools, and demonstrate its applicability on an example EnergyPlus model.

Automated Continuous Commissioning of Commercial Buildings

“…Derivative based sensitivities [23] are calculated for sensitivity analysis. Details about the approach can be found from the related publication [24].…”

Review on stochastic modeling methods for building stock energy prediction

“…It uses deterministic simulation models to perform stochastic analysis. It performs an uncertainty analysis or sensitivity analysis through the Monte Carlo sampling method (Lomas and Eppel 1992;Macdonald 2002;de Wit and Augenbroe 2002;Reddy et al 2007;Hopfe 2009;Corrado and Mechri 2009;Eisenhower et al 2012a). In this approach, the values of input parameters are sampled randomly from a given range and then feed into the energy model.…”

Bayesian calibration of building energy models with large datasets

“…Macdonald and Strachan (2001) reviewed uncertainties in the thermophysical properties of construction materials and incorporated them into the building energy simulation tool ESP-r using Monte Carlo Analysis. Eisenhower et al (2012a) modeled 1009 EnergyPlus input parameters as uncertain by varying them ±20% of their nominal value. Using sensitivity analysis, they provided insights to how uncertainty in input parameters may affect model output.…”