simulation, ensembles, uncertainty
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 a building envelope’s thermal conductivity properties for a heavily instrumented residential building involving more than 200 sensors. A total of 156 input parameters were determined to be important by experts, which were then varied using a Markov Order process. 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. This paper discusses different strategies one could employ when the number of simulation range from a few to tens of thousands of simulations in an ensemble. The paper highlights this and the associated computational challenge in the context of ensemble simulations where the chosen sampling process allows one to generate datasets consisting of just of a few simulations to an exponentially large intractable dataset with data in the hundreds of terabytes. Besides the computational and data management challenges, the paper will also presents meaningful visualization approaches that are candidates for extreme scale analysis. The method of analysis almost always depends on the experimental design. While Markov Ordering for sampling will be explicitly presented, the paper will also touch upon various other experimental design strategies and their resulting analysis methods in the context of scientific simulations. We expect the sampling and ensemble analysis at various scales to help us gain insight into unique issues of building energy modeling, especially at different scales of simulation. We also expect the analytic approaches employed for understanding the thermal properties of building envelopes to be beneficial for software calibration and building design. We demonstrate these in the context of a real-world, heavily instrumented building.
Uncertainty Analysis of a Heavily Instrumented Building at Different Scales of Simulation