Advanced Building Simulation

172 Mahdavi
7.4.2 Approach
Modern buildings allow, in principle, for multiple ways to achieve desired environmental
conditions. For example, to provide a certain illuminance level in an office,
daylight, electrical light, or a combination thereof can be used. The choice of the
system(s) and the associated control strategies represent a nontrivial problem
since there is no deterministic procedure for deriving a necessary (unique) state of
the building’s control systems from a given set of objective functions (e.g. desirable
environmental conditions for the inhabitants, energy and cost-effectiveness of the
operation, minimization of environmental impact).
Simulation-based control can potentially provide a remedy for this problem (Mahdavi
1997a, 2001a; Mahdavi et al. 1999a, 2000). Instead of a direct mapping attempt from
the desirable value of an objective function to a control systems state, the simulationbased
control adopts an “if-then” query approach. In order to realize a simulation-based
building systems control strategy, the building must be supplemented with a multi-aspect
virtual model that runs parallel to the building’s actual operation. While the real building
can only react to the actual contextual conditions (e.g. local weather conditions, sky
luminance distribution patterns), occupancy interventions, and building control operations,
the simulation-based virtual model allows for additional operations: (a) the virtual
model can move backward in time so as to analyze the building’s past behavior and/or
to calibrate the program toward improved predictive potency; (b) the virtual model can
move forward in time so as to predict the building’s response to alternative control scenarios.
Thus, alternative control schemes may be evaluated, and ranked according to
appropriate objective functions pertaining to indoor climate, occupancy comfort, as well
as environmental and economic considerations.
7.4.3 Process
To illustrate the simulation-based control process in simple terms, we shall consider
four process steps (cp. Table 7.4):
1 The first step identifies the building’s control state at time t i within the applicable
control state space (i.e. the space of all theoretically possible control states).
For clarity of illustration, Table 7.4 shows the control state space as a threedimensional
space. However, the control state space has as many dimensions as
there are distinct controllable devices in a building.
2 The second step identifies the region of the control state space to be explored in
terms of possible alternative control states at time t i�1.
3 The third step involves the simulation-based prediction and comparative ranking
of the values of pertinent performance indicators for the corpus of alternative
identified in the second step.
4 The fourth step involves the execution of the control action, resulting in the
transition of control state of the building to a new position at time t i�1.
7.4.4 An illustrative example
Let us go through the steps introduced in Section 7.4.3 using a demonstrative experiment
regarding daylighting control in an office space in the previously mentioned IW