Advanced Building Simulation

18 Augenbroe
for commercial offerings. The opportunities to store and reuse audit-trails of user
runs on the server are other benefits.
The model on the server can be made “invisible,” which is useful when the user
does not interact with the simulation directly but only indirectly, for instance,
through a web-enabled decision support environment. An example of such application
is reported by Park et al. (2003) in an Intranet enabled control system for smart
façade technologies. The approach uses an “Internet ready” building system that
plugs into the Internet making it instantly accessible from any location. In this
instance, the model performs autonomous simulations in response to proposed user
control interventions.
In the future, simulation may be part of an e-business service, such as the webhosted
electronic catalogue of a manufacturer of building components. Each product
in the catalogue could be accompanied by a simulation component that allows users
to inspect the product’s response to user-specified conditions. Web hosting makes
a lot of sense in this case, as manufacturers are reluctant to release the internal physical
model with empirical parameters of a new product to the public. Taking it one
step further, the simulation component may be part of the selling proposition and will
be available to a buyer for downloading. This will for instance allow the component
to be integrated into a whole building simulation. Alternatively, the component could
remain on the server and be made to participate in a distributed simulation.
Obviously, there are important model fidelity issues like applicability range and
validation that need to be resolved before this simulation service could gain broad
acceptance. Jain and Augenbroe (2003) report a slight variation on this theme. In
their case, the simulation is provided as a service to rank products found in e-catalogues
according to a set of user-defined performance criteria.
1.4.2 Performance requirement driven simulation
In the previous subsection the focus was on delivering federated or web-hosted
simulation functions. Another potentially important manifestation of simulation is
the automated call of simulation to assess normative building performance according
to predefined metrics. This approach could become an integral part of performancebased
building methods that are getting a lot of attention in international research
networks (CIB-PeBBu 2003). Performance-based building is based on a set of standardized
performance indicators that constitute precisely defined measures to express
building performance analysis requests and their results. These performance requirements
will become part of the formal statement of requirements (SOR) of the client.
They are expressed in quantified performance indicators (PIs). During design evolution,
the domain expert analyzes and assesses the design variants against a set of predefined
PIs addressed in the SOR, or formulated during the design analysis dialogue,
that is, expressed by the design team as further refinements of the clients requirements.
In this section a theoretical approach is discussed which could effectively
underpin performance-based design strategies by performance metrics that are based
on simulation. At this point of time no systems exist to realize this, although an early
try is reported by Augenbroe et al. (2004).
The role of simulation during this process could be centered around a (large) set of
predefined PIs. Every PI is an unambiguously defined measure for the performance