Simulation plays a big role in understanding the behavior of building envelopes. With the increasing availability of computational resources, it is feasible to conduct parametric simulations for applications such as software model calibration, building control optimization, or fault detection and diagnostics. In this paper, we present an uncertainty exploration of two types of buildings: a) of a building envelope's thermal conductivity properties for a heavily instrumented residential building involving more than 200 sensors, and b) a sensitivity analysis of a stand-alone retail building from the U.S. Department of Energy's reference model. A total of 156 input parameters were determined to be important by experts which were then varied using a Markov Order process for the residential building generating hundreds of GBs of data for tens of thousands of simulations. For the commercial building, 20 parameters were varied using a fractional factorial design requiring just 1024 simulations generating data in the order of a few hundred megabytes. These represent a wide variety and range of simulations from a few to tens of thousands of simulations in an ensemble. Depending on the number of simulations in an ensemble, the techniques employed to meaningfully make sense of the information can be very different, and potentially challenging. Additionally, the method of analysis almost always depends on the experimental design. The Markov Order sampling strategy and fractional factorials designs of sampling presented represent two approaches one could employ for large sensitivity analysis of buildings at two different scales of simulations. The paper presents the analysis using descriptive statistics as well as employing multiple analysis of variance techniques for comparison and contrast.
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