Civil Engineering Reference
In-Depth Information
communication among them. Thus, whereas the DMPC obtains similar results as
the centralised one, the decentralised MPC generates a worst performance. Similar
works by the same authors are Morosan et al. (
2010b
,
2011a
,
b
).
Another issue being dealt with in recent years is to use the price of energy to reduce
energy consumption, by penalising the use of the HVAC system when the energy
price is the most expensive. Some versions of this issue can be found in Agüero et
al. (
2013a
), Oldewurtel et al. (
2010b
,
2011
), Vrettos et al. (
2013
). In what follows
the work in Oldewurtel et al. (
2010b
) is summarised, whereas in Sect.
6.3
the work
in Agüero et al. (
2013a
) is discussed and some of its results are shown.
A method of reducing energy demand in buildings using real-time electricity
energy price is presented inOldewurtel et al. (
2010b
). Themethod is used to fulfil with
the minimum energy the aim to keep room temperature, illuminance and CO
2
levels
within a given comfort range. Anewdeveloped time-varying, hourly based electricity
tariff for end-consumers based on spot market prices as well as on electricity grid
load levels, is used in the cost function of an MPC algorithm. Since this electricity
tariff is only available for a limited time window in the future a least-squares support
vector machines for electricity tariff price forecasting is used to provide a prediction
of the estimated time-varying costs for the whole prediction horizon. Thus, the work
proposes to use model MPC and a time-varying tariff scheme that is based both on
spot market prices as well as on actual electricity grid load levels. The performance of
the proposed control strategy is presented through the evolution of room temperature
for three different cases: (i) an MPC optimisation using a constant electricity tariff,
(ii) an MPC optimisation using the variable tariff using the regression estimation and
(iii) an MPC optimisation assuming perfect information about electricity price. For
the interested reader, these authors have other works which deal with this issue and
can be found in Oldewurtel et al. (
2011
), Vrettos et al. (
2013
).
The mean disturbance that is presented to control thermal comfort or air quality
is the people who are inside the room or building since their presence has influence
on both temperature and CO
2
concentration. A new research line has recently been
openedwhich deals withRepetitive Control (RC) or Iterative LearningControl (ILC).
Repetitive control bases its performance on the introduction of a generator of the
periodic signal to be tracked/rejected inside the controller. The key idea is that the
performance of a system that executes the same task multiple times can be improved
by learning from previous executions (iterations); this is the essence of RC. In this
case the periodic signal to counteract is the entries and outputs of the people. In some
buildings, as labs or offices, people enter and leave the building following certain
patterns which is repeated from one day to another. Thus, this behaviour can be
considered periodic and can be counteracted through an RC. Unlike other comfort
control issues described previously, in this case there are not many works which
address this matter, but the reader can find some examples in Álvarez et al. (
2013
)
and Vinther et al. (
2013
).
Finally, other works which deal with users' comfort control are summarised.
In Oldewurtel et al. (
2010a
,
2012
) the concept of Integrated RoomAutomation (IRA)
is introduced. IRA uses MPC and weather predictions to control thermal and visual
comfort and air quality increasing the energy efficiency. Another work that deals with
