  It is necessary to estimate the expected energy usage of a building to
determine how to reduce energy usage. The expected energy usage of a building
can be reliably simulated using a Building Energy Model (BEM). Many of the
numerous input parameters in a BEM are uncertain. To ensure that the building
simulation is sufficiently accurate, and to better understand the impact of
imprecisions in the input parameters and calculation methods, it is desirable
to quantify uncertainty in the BEM throughout the modeling process. Uncertainty
quantification (UQ) typically requires a large number of simulations to produce
meaningful data, which, due to the vast number of input parameters and the
dynamic nature of building simulation, is computationally expensive.
Uncertainty Quantification (UQ) in BEM domain is thus intractable due to the
size of the problem and parameters involved and hence it needs an advanced
methodology for analysis. The current paper outlines a novel
Weakly-Connected-Systems (WCSs) identification-based UQ framework developed to
propagate the quantifiable uncertainty in the BEM. The overall approach is
demonstrated on the physics-based thermal model of an actual building in
Central New York.
