Civil Engineering Reference
In-Depth Information
algorithm consists of reducing the water flow for room 2 (green dashed line in the
third graph), where the fancoil air velocity is not saturated, to increase the water flow
in rooms 1 and 3 (blue solid line and red dashed-dotted line in the same graph) and
hence, to be able to counteract the effects of the people. It is worth mentioning that
the water flow increment in room 1 is larger than the one in room 3. Of course, it
makes sense, since the thermal conditions in room 1 are worse than the ones in room
3, at this time, mainly because there are more people inside room 1. This can be
observed in the bottom graph of Fig.
5.42
. Notice that the PMV index value for room
1 (blue solid line) is slightly greater than the one for room 3 (red dashed-dotted line),
see top graph of Fig.
5.42
.
At time
t
2450 min, three people leave room 1, which causes that the thermal
conditions in room 1 become better than the ones in room 3 at time
t
=
2550 min.
As can be seen in the top graph on Fig.
5.42
, the PMV index for room 1 is closer to
zero than the one for room 3, and once again the HVAC system is at full capacity. For
this reason, at this time, the optimisation algorithm must propose a solution which
reduces the water flow supplied for room 1 in favour of room 3. With those actions,
the optimiser helps to obtain PMV indices close to zero for the whole set of rooms.
At the end of the second simulation day, people leave the three rooms until they are
completely empty. Then, the optimisation algorithm can achieve a PMV index equal
to zero by decreasing the air velocity of those rooms.
As a conclusion, thanks to the optimisation algorithm, the PNMPC has been able
to maintain the thermal comfort in all the rooms, reaching PMV index values lower
than 0
=
.
1 during the whole simulation. In fact, when the equal constraint is satisfied,
i.e. the sum of the water flows of all the rooms is equal to 60 1
/
min, the pair PNMPC
controller-optimizer behaves as expected, proving reasonable solutions which induce
PMV indices close or equal to zero. Finally, the interested reader is referred toÁlvarez
et al. (
2013
) where more information about this work and more simulation tests can
be found.
5.7 Conclusion
This chapter deals with both thermal comfort and indoor air quality control by means
of PMV and IAQ indices, respectively. The proposed controllers generate indoor air
temperature and window aperture setpoints inside a classical control loop.
MPC is one of the most extended techniques for comfort control since it uses
a model of the system, noise and disturbances to perform predictions of the future
output. These predictions are incorporated within a cost function which is related
to closed loop behaviour and control effort, and that is minimised as a function of
the future control signals sequence taking into account constraints defined in the
problem. Finally, a receding horizon strategy is implemented to achieve feedback.
It consists of that at each time the horizon is displaced towards the future, which
involves the application of the first control signal of the sequence calculated at each
step while the remaining signals are not used. At the next control instant, the horizon
