Interactive Building Design Space Exploration Using Regionalized Sensitivity Analysis

Monte Carlo simulations combined with regionalized sensitivity analysis provide the means to explore a vast, multivariate design space in building design. Typically, sensitivity analysis shows how the variability of model output relates to the uncertainties in models inputs. This reveals which simulation inputs are most important and which have negligible influence on the model output. Popular sensitivity methods include the Morris method, variance-based methods (e.g. Sobol’s), and regression methods (e.g. SRC). However, all these methods only address one output at a time, which makes it difficult prioritize and fixate inputs when considering multiple outputs. In this work, the primary outcome is a methodology to apply Kolmogorov-Smirnov two-sample (KS2) statistics to rank inputs due to their influence with respect to multiple outputs. A secondary outcome is the application of KS2 statistics in combination with the interactive parallel coordinate plot (PCP). The latter is an effective tool to explore stochastic simulations and to find high-performing building designs. The proposed methods help decision makers to focus their attention to the most important design parameters when exploring a multivariate design space. As case study, we consider building performance simulations of a 15.000 m² educational centre with respect to energy demand, thermal comfort, and daylight.