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simply specifying look-up tables. The former modeling approach is
usually used for secondary system component descriptions, while for
primary system components, due to their complexity, the latter
approaches are more often used, but exceptions exist [29].
3.2. Modeling approaches for HVAC control
HVAC controllers can be pided into two categories as follows.
Local controllers are low-level controllers that allow HVAC systems
to operate properly and to provide adequate services. Local controllers
can be further subpided into two groups [30]:
• Sequencing controllers define the order and conditions associated
with switching equipment ON and OFF. Typical sequencing controllers
in HVAC systems are chiller sequencing controller, cooling tower
sequencing controller, pump sequencing controller, fan sequencing
controller, etc.
• Process controllers adjust the control variables to meet the
required set point in spite of disturbances and considering the system
dynamic characteristics. The typical process controllers used in the
HVAC field are P, PI, PID, ON/OFF, step controller, etc.
Supervisory controllers are high level controllers that allow
complete consideration of the system level characteristics and
interactions among all components and their associated variables.
For example, a supervisory controller sets operation modes and sets
points for local controllers.
From a modeling point of view, controllers are represented by
equations that must be satisfied in every simulation step. The
controllers direct the interaction between building and system as
well as interactions between components within the system.
In reality, the closed-loop local-process control includes a sensor
that samples a real-world (measurable) variable. The controller, based
on the set point value and measured value, and according to the
controller-specific control algorithm, calculates the control signal that
feeds the real-world actuator. However, in the simulation tool the
user can address variables that cannot be sensed or actuated in reality,
as well as apply control algorithms that do not exist in reality. For
example, a modeler can directly actuate the heat flux in a model
where in reality this could only be done indirectly by changing a
valve/damper position.
Furthermore, due to the accessibility ofmany variables not directly
known in the real world, such as the zone cooling/heating load, in
simulation the concept of “ideal” (local process) control becomes
feasible. An “ideal” local-process controller means that the actuated
variable will be adjusted to satisfy the set point requirements for the
controlled variable, without specifying the explicit control algorithm
and by numerically inverting the (forward) simulation models (from
the required output calculate the input needed to satisfy this).
Possibilities to simulate different (advanced) controllers in state-
of-the-art BPS tools are limited. Some tools offer pre-defined control
strategies (system-based simulation tools), some offer flexibility in
specifying only supervisory controllers (EnergyPlus) and some even
in specifying local controllers (TRNSYS, ESP-r). The domain-indepen-
dent environments, such as MATLAB and Dymola, are efficient tools
for designing and testing of controllers in a simulation setting, but lack
the models of all other physical phenomena in buildings.
3.3. Modeling approaches for HVAC systems
Hensen [31] defines four categories of HVAC system representa-
tion in BPS tools, ranging from purely conceptual towards more
explicit, as follows.
Pure conceptual system modeling approach represents the case
where only room processes are considered, while all other processes
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