Advanced Building Simulation

56 de Wit
information in decision-making. In the context of an example case the structure of an
uncertainty analysis was explained, including assessment of the uncertainty in model
parameters, propagation of the uncertainty and sensitivity analysis. It was shown
how the uncertainty analysis can be specifically refined, based on the results of the
sensitivity analysis, using structured expert judgment studies. Finally, this chapter discussed
how Bayesian decision theory can be applied to make more rational building
simulation informed decisions with explicit uncertainty information.
If explicit appraisal of uncertainty is to pervade building simulation, especially in
practical settings, several challenges have to be dealt with:
● Simulation tools: the functionality of most building simulation tools needs
enhancement to facilitate uncertainty and sensitivity analysis.
● Databases: information about uncertainties in model parameters and scenarioelements
should be compiled and made available at the fingertips of consultants,
who perform building simulation in practical settings.
● Decision support: a full Bayesian decision analysis is too laborious for mainstream
application. A quick scan method would be indispensable to distinguish the more
complex decision-problems from the “clear-cut” cases as sharply as possible.
● Expertise: to adequately analyze and use uncertainty information, consultants
and building simulationists would require some background in the fields of
statistics, probability theory, and decision-making under uncertainty.
This chapter has mainly focused on uncertainty in the context of decision-making.
However, the notions and techniques explicated here can also make a contribution in
the development and validation of building simulation models. Specific attention can
be given to those parts of the model, which give a disproportionate contribution to
the uncertainty. If a model part causes too much uncertainty, measures can be considered
such as more refined modeling or collection of additional information by, for
example, an experiment. On the other hand, model components that prove to be
overly sophisticated may be simplified to reduce the time and effort involved in
generating model input and running the computer simulations.
It is worthwhile to explore how these ideas could be practically elaborated.
Notes
1 Note that ‘simulation’ in Monte Carlo simulation refers to statistical simulation, rather than
building simulation.
2 The wind pressure difference coefficient is the difference between the pressure coefficient for
the window of modeled building section in the west façade and the one for the window in
the east façade.
3 The hypothetical experiments are physically meaningful, though possibly infeasible for
practical reasons.
References
Allen, C. (1984). “Wind pressure data requirements for air infiltration calculations.” Report AIC-
TN-13-84 of the International Energy Agency—Air Infiltration and Ventilation Centre, UK.
Andres, T.H. (1997). “Sampling methods and sensitivity analysis for large parameter sets.”
Journal of Statistical Computation and Simulation, Vol. 57, No. 1–4, pp. 77–110.