Comparison of sampling techniques on the performance of Monte-Carlo based sensitivity analysis

Sensitivity analysis is a key part of a comprehensive energy simulation study.  Monte-Carlo techniques have been successfully applied to many simulation tools. Several sampling techniques have been proposed in the literature; however to date there has been no comparison of their performance for typical building simulation applications. This paper examines the performance of simple random, stratified and Latin Hypercube sampling when applied to a typical building simulation problem.  An integrated natural ventilation problem was selected as it has an inexpensive calculation time thus allowing multiple sensitivity analyses to be undertaken, while being realistic as wind and temperature effects are both modeled. The research shows that compared to simple random sampling: LHS and stratified sampling produce results that are not significantly different (at a 5% level) with increased robustness (less variance in the mean prediction).  However, it should not be inferred from this that fewer simulation runs are required for LHS and stratified sampling.  Given the results presented here and in previous work it would indicate that for practical purposes Monte-Carlo uncertainty analysis in typical building simulation applications should use about 100 runs and simple random sampling.