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and the analysis of the results more complicated.
Most design analyses do not require detailed systemmodeling and
simulation as the energy consumption can be estimated by using
simpler modeling approaches. The conceptual system representation
shows its advantages (lower required user expertise, lesser input
data, less intense computations, easier results analysis, etc.) when
only load predictions are considered, and/or when energy savingoptions are investigated. However, for comparing HVAC system alter-
natives and evaluating different control strategies [18,51] detailed
HVAC system models are required. In the system-based modeling
approach, the speed of system alternatives evaluation is much higher
than in the component based modeling approach, but the investiga-
tion of innovative technologies is limited.
Matching the applicabilities of system modeling approaches with
the design questions at hand, the user can benefit from both ease o
the former categories and flexibility of the latter ones. However
building a right model for a simulation task at hand is still more an art
than an engineering discipline. This issue is highly relevant when
there is no (measured) data which can be used for direct mode
accuracy evaluation. Thus, in this case the model adequacy for the
particular simulation objective needs to be evaluated differently.
Building a right system model for a specific purpose is to require
that the modeling validity and data validity match as far as possible
the required validity [52]. The required validity is assessed only
against those aspects of the real world that are of relevance for
successful accomplishment of simulation objectives, represented by
performance parameters.
Model complexity can be expressed in terms of scope (defined by a
number of components in the model) and resolution (defined by a
number of states per component in the model) of the model and
interactions among components in themodel. Abstraction is a genera
process and includes various simplification approaches with regards
to system boundaries considered, number of modeled physica
phenomena, the resolution of modeling of each considered phenom-
enon, etc. in increase in model complexity increases the cost of using
the model. Thus, the model should be of the lowest complexity while
preserving its validity for the intended simulation objectives. The
required lowest model complexity depends on the simulation
objective. Also, increasing the complexity, for different simulation
objectives, has different implications for the value of the model to the
user, as schematically shown in Fig. 1. For different simulation
objectives the model cost exceeds the model value to the user at
different model complexities. For some objectives the cost of the
model will exceed its value even when the modeling complexity is
low, and for some, the simulation objective can justify the use ofmore
complex models. Moreover, the rate of change in the model value can
be different for different simulation objectives at different complex-
ities. On the one hand, a simple model can have a high value at low
modeling complexity for some simulation objectives; this valuemight
not be increased by increasing the complexity. On the other hand, amodel has a value only above certain modeling complexity for some
other simulation objectives, as illustrated in Fig. 1.
The potential techniques that can be used to ease selection of
modeling complexity for a particular simulation objective can be
described in following paragraphs (the use of the techniques has been
reported previously [53,54]).
Definition of the minimum required modeling complexity can be
accomplished by using the checklist rationale from [55] represented
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