Advanced Building Simulation

176 Mahdavi
appropriate correction factors may be derived based on statistical methods and
neural network applications.
2 Second, preparation of complete and valid input data (geometry, materials,
system specifications) for simulation is often a time-consuming and error-prone
task. In the context of self-organizing models, however, such data would be prepared
mostly in an automated (sensor-based) fashion, thus reducing the need for
human intervention toward periodic updating of simulation models.
In the following discussion, we focus on arguably the most daunting problem of the
simulation-based control strategy, namely the rapid growth of the size of the control
state space in all those cases where a realistic number of control devices with multiple
possible positions are to be considered.
Consider a space with n devices that can assume states from s 1 to s n. The total
number, z, of combinations of these states (i.e. the number of necessary simulation
runs at each time-step for an exhaustive modeling of the entire control state space) is
thus given by:
z � s 1, s 2,…, s n
(7.3)
This number represents a computationally insurmountable problem, even for a
modest systems control scenario involving a few spaces and devices: An exhaustive
simulation-based evaluation of all possible control states at any given time-step
is simply beyond the computational capacity of currently available systems. To
address this problem, multiple possibilities must be explored, whereby two general
approaches may be postulated, involving: (i) the reduction of the size of the control
state space region to be explored, (ii) the acceleration of the computational assessment
of alternative control options.
7.4.5.2 The control state space
At a fundamental level, a building’s control state space has as many dimensions as
there are controllable devices. On every dimension, there are as many points as there
are possible states of the respective device. This does not imply, however, that at every
time-step the entire control state space must be subjected to predictive simulations.
The null control state space. Theoretically, at certain time-steps, the size of the
applicable control state space could be reduced to zero. Continuous time-step performance
modeling is not always necessary. As long as the relevant boundary conditions
of systems’ operation have remained either unchanged or have changed only
insignificantly, the building may remain in its previous state. Boundary conditions
denote in this case factors such as outdoor air temperature, outdoor global horizontal
irradiance, user request for change in an environmental condition, dynamic
change in the utility charge price for electricity, etc. Periods of building operation
without significant changes in such factors could reduce the need for simulation and
the associated computational load.
Limiting the control state space. Prior to exhaustive simulation of the theoretically
possible control options, rules may be applied to reduce the size of the control