Advanced Building Simulation

32 de Wit
result. In fact, economy is one of the important motives in the ongoing development
of new propagation techniques. More economic or efficient methods often rely on
specific assumptions about the model behavior such as linearity or smoothness in the
parameters. To obtain reliable results with such methods, it is important to verify
whether these assumptions hold for the model at hand.
In practical situations, an additional aspect of interest is commonly the ease and
flexibility to apply the method to the (simulation) model.
SELECTED TECHNIQUE IN THE CASE EXAMPLE
The purpose of the propagation in the example case is to estimate the mean and standard
deviation of the model output (building performance), and to obtain an idea of
the shape of the probability distribution. The most widely applicable, and easy to
implement method for this purpose is Monte Carlo simulation. 1 It has one drawback:
it requires a large number of model evaluations. In the example case this is not a big
issue. Obviously, if computationally intensive models are to be dealt with (e.g.
Computational Fluid Dynamics, CFD), this will become an obstacle.
In the example case, however, we will use Monte Carlo (MC) simulation. To somewhat
speed up the propagation, a modified Monte Carlo technique will be applied, that
is, Latin Hypercube Sampling (LHS). This is a stratified sampling method. The domain
of each parameter is subdivided into N disjoint intervals (strata) with equal probability
mass. In each interval, a single sample is randomly drawn from the associated probability
distribution. If desired, the resulting samples for the individual parameters can be
combined to obtain a given dependency structure. Application of this technique provides
a good coverage of the parameter space with relatively few samples compared to
simple random sampling (crude Monte Carlo). It yields an unbiased and often more
efficient estimator of the mean, but the estimator of the variance is biased. The bias is
unknown, but commonly small. More information can be found in, for example,
McKay et al. (1979), Iman and Conover (1980), and Iman and Helton (1985).
2.3.2.4 Sensitivity analysis
In the context of an uncertainty analysis, the aim of a sensitivity analysis is to
determine the importance of parameters in terms of their contribution to the uncertainty
in the model output. Sensitivity analysis is an essential element in a cyclic
uncertainty analysis, both to gain understanding of the makeup of the uncertainties
and to pinpoint the parameters that deserve primary focus in the next cycle of the
analysis.
Especially in first stages of an uncertainty analysis only the ranking of parameter
importance is of interest, rather than their absolute values. To that purpose, crude
sensitivity analysis techniques are available, which are also referred to as parameter
screening methods.
SELECTION CRITERIA FOR A PARAMETER SCREENING TECHNIQUE
Techniques for sensitivity analysis and parameter screening are well documented
in the literature, for example, in Janssen et al. (1990), McKay (1995), Andres (1997),