Advanced Building Simulation

178 Mahdavi
7.4.5.3 Efficient assessment of alternative control options
Our discussions have so far centered on the role of detailed performance simulation
as the main instrument to predict the behavior of a building as the result of alternative
control actions. The obvious advantage of simulation is that it offers the possibility
of an explicit analysis of various forces that determine the behavior of the
building. This explicit modeling capability is particularly important in all those cases,
where multiple environmental systems are simultaneously in operation. The obvious
downside is that detailed simulation is computationally expensive. We now briefly
discuss some of the possible remedies.
Customized local simulation. As mentioned earlier, simulation functionality may
be distributed across multiple control nodes in the building controls system. These
distributed simulation applications can be smaller and be distributed across multiple
computing hardware units. Running faster and on demand, distributed simulation
codes can reduce the overall computational load of the control system.
Simplified simulation. The speed of simulation applications depends mainly on
their algorithmic complexity and modeling resolution. Simpler models and simplified
algorithms could reduce the computational load. Simplification and lower level of
modeling detail could of course reduce the reliability of predictions and must be thus
scrutinized on a case-by-case basis.
Simulation substitutes. Fundamental computational functionalities of detailed
simulation applications may be captured by computationally more efficient regression
models or neural network copies of simulation applications. Regression models
are derived based on systematic multiple runs of detailed simulation programs and
the statistical processing of the results. Likewise, neural networks may be trained by
data generated through multiple runs of simulation programs. The advantage of these
approaches lies in the very high speed of neural network computing and regression
models. Such modeling techniques obviously lack the flexibility of explicit simulation
methodology, but, if properly engineered, can match the predictive power of detailed
simulation algorithms. Multiple designs of hybrid control systems that utilize
both simulation and machine learning have been designed and successfully tested
(Chang and Mahdavi 2002).
Rules represent a further class of—rather gross—substitutes for simulation-based
behavioral modeling. In certain situations, it may be simpler and more efficient to
describe the behavior of a system with rules, instead of simulations. Such rules could
define the relationship between the state of a device and its corresponding impact on
the state of the sensor. Rules can be developed through a variety of techniques. For
example, rules can rely on the knowledge and experience of the facilities manager, the
measured data in the space to be controlled, or logical reasoning.
7.4.6 Case studies
7.4.6.1 Overview
To provide further insights into the problems and promises of simulation-based control
strategies, we present in the following sections, two illustrative case studies involving