Advanced Building Simulation

and photometric properties, as well as the outdoor measurements at time interval t i
were used as model input). Based on the simulation results and objective functions, it
was possible to determine for each time-step the louver position that was considered
most likely to maximize the light distribution uniformity or to minimize the deviation
of average illuminance from the target value.
Step 4. Device controller instructed the control device (louver) to assume the position
identified in step 3 as most desirable.
To evaluate the performance of the simulation-based control approach in this particular
case, we measured during the test period at each time-step the resulting illuminance
levels sequentially for all four louver positions and for all selected time
intervals. To numerically evaluate the performance of this simulation-based control
approach via a “control quality index”, we ranked the resulting (measured) average
illuminance and the uniformity according to the degree to which they fulfilled the
objective functions. We assigned 100 points to the instances when the model-based
recommendation matched the position empirically found to be the best. In those cases
where the recommendation was furthest from the optimal position, control quality
index was assumed to be zero. Intermediate cases were evaluated based on interpolation.
Control quality index was found to be 74 for illuminance and 99 for uniformity.
The better performance in the case of the uniformity indicator is due to the
“relative” nature of this indicator, which, in contrast to the illuminance, is less
affected by the absolute errors in the predictions of the simulation model.
7.4.5 Challenges
Self-organizing models for sentient buildings 175
7.4.5.1 Introduction
In previous sections we described the simulation-based strategy toward building systems
control and how this approach, supported by a self-organizing building model,
could facilitate the operation of a sentient building. The practical realization of these
methods and concepts, however, requires efficient solutions for various critical implementation
issues. The appendices of the chapter include case studies involving
demonstrative implementation efforts that illustrate some of these problems and their
potential solutions.
There are two basic problems of the proposed approach, which we briefly mention
but will not pursue in detail, as they are not specific to simulation-based control
methodology but represent basic problems related to simulation methods and
technologies in general:
1 First, the reliability of simulation algorithms and tools is always subject to
validation, and this has been shown to be a difficult problem in the building performance
simulation domain. In the context of sentient building implementations,
there is an interesting possibility to improve on the predictive capability of
the simulation applications by “on-line” calibration of simulation results. This
can be done by continuous real-time monitoring of the performance indicator
values (using a limited number of strategically located sensors) and comparing
those with corresponding simulation results. Using the results of this comparison,