Advanced Building Simulation

Self-organizing models for sentient buildings 177
state space to one of practical relevance. Such rules may be based on heuristic and
logical reasoning. A trivial example of rules that would reduce the size of the control
state space would be to exclude daylight control options (and the corresponding
simulation runs) during the night-time operation of buildings’ energy systems.
Compartmentalization. The control state space may be structured hierarchically,
as seen in Section 7.3. This implies a distribution of control decision-making across
a large number of hierarchically organized decision-making nodes. We can imagine
an upward passing of control state alternatives starting from low-level DCs to upperlevel
MCs. At every level, a control node accesses the control alternatives beneath and
submits a ranked set of recommendations above. For this purpose, different methods
may be implemented in each node, involving rules, tables, simulations, etc.
Simulation routines thus implemented, need not cover the whole building and all the
systems. Rather, they need to reflect behavioral implications of only those decisions
that can be made at the level of the respective node.
“Greedy” navigation and random jumps. Efficient navigation strategies can help
reduce the number of necessary parametric simulations at each time-step. This is independent
of the scale at which parametric simulations are performed (e.g. whole-building
simulation versus local simulations). In order to illustrate this point, consider the
following simple example: Let D be the number of devices in a building and P the number
of states each device can assume. The total number z of resulting possible combinations
(control states) is then given by Equation (7.4).
z � P D (7.4)
For example, for D � 10 and P � 10, a total of 10 billion possible control states
results. Obviously, performing this number of simulations within a time-step is not
possible. To reduce the size of the segment of the control state space to be explored,
one could consider, at each time-step, only three control states for each device,
namely the status quo, the immediate “higher” state, and the immediate “lower”
state. In our example, this would mean that D � 10 and P � 3, resulting in 59,049
control states. While this result represents a sizable reduction of the number of simulation,
it is still too high to be of any practical relevance. Thus, to further reduce the
number of simulations, we assume the building to be at control state A at time t 1. To
identify the control state B at time t 2, we scan the immediate region of the control
state space around control state A. This we do by moving incrementally “up” and
“down” along each dimension, while keeping the other coordinates constant.
Obviously, the resulting number of simulations in this case is given by:
z � 2D � 1 (7.5)
In our example, D � 10. Thus, n � 21. Needless to say, this number represents a
significantly more manageable computational load. However, this “greedy” approach to
control state space exploration obviously bears the risk that the system could be caught
in a performance state corresponding to a local minima (or maxima). To reduce this
risk, stochastically based excursions to the more remote regions of the control state
space can be undertaken. Such stochastic explorations could ostensibly increase the possibility
of avoiding local minima and maxima in search for optimal control options.