Advanced Building Simulation

This completes the outline of the case that will be used as an example throughout
this chapter. The case concerns a decision-problem, the analysis of which requires
assessment of the consequences of a particular action in terms of a building performance
indicator. This assessment involves uncertainty. The analysis of this uncertainty
is the topic of the next section.
2.3 Uncertainty analysis
Uncertainty in building simulation 29
2.3.1 Introduction
Uncertainty may enter the assessment of building performance from various sources.
First, the design specifications do not completely specify all relevant properties of the
building and the relevant installations. Instead of material properties, for instance,
material types will commonly be specified, leaving uncertainty as to what the exact
properties are. Moreover, during the construction of the building, deviations from the
design specifications may occur. This uncertainty, arising from incomplete specification
of the system to be modeled will be referred to as specification uncertainty.
Second, the physical model development itself introduces uncertainty, which we
will refer to as modeling uncertainty. Indeed, even if a model is developed on the basis
of a complete description of all relevant building properties, the introduction of
assumptions and the simplified modeling of (complex) physical processes introduce
uncertainty in the model.
Third, numerical errors will be introduced in the discretization and simulation of
the model. We assume that this numerical uncertainty can be made arbitrarily small
by choosing appropriate discretization and time steps. Hence, this uncertainty will
not be addressed here.
Finally, uncertainty may be present in the scenario, which specifies the external
conditions imposed on the building, including for example outdoor climate conditions
and occupant behavior. The scenario basically describes the experiment, in
which we aim to determine the building performance.
To quantitatively analyze uncertainty and its impact on building performance, it
must be provided with a mathematical representation. In this study, uncertainty is
expressed in terms of probability. This representation is adequate for the applications
of concern in this work and it has been studied, challenged, and refined in all its
aspects.
Moreover, in interpreting probability, we will follow the subjective school. In the
subjective view, probability expresses a degree of belief of a single person and can, in
principle, be measured by observing choice behavior. It is a philosophically sound
interpretation, which fulfills our needs in decision analysis.
It should be mentioned, however, that in the context of rational decision-making,
one subjective probability is as good as another. There is no rational mechanism for
persuading individuals to adopt the same degree of belief. Only when observations
become available, subjective probabilities will converge in the long run. However, the
aim of uncertainty analysis is not to obtain agreement on uncertainties. Rather, its
purpose is to explore the consequences of uncertainty in quantitative models.
Discussions and background on the interpretation of probability can be found in for
example Savage (1954), Cooke (1991), and French (1993).