Civil Engineering Reference
In-Depth Information
Nevertheless, the most used technique to control visual comfort is based on fuzzy
logic. There are several works in literature which make use of this kind of method-
ology to maintain users' comfort since it can easily represent the human reasoning
through simple rules (Cziker et al.
2007
; Dounis et al.
1993
,
2011
; Trobec et al.
2005
;
Yifeietal.
2009
). Furthermore, this technique can be used in combination with clas-
sical control techniques, as in Ciabattoni et al. (
2009
) where a smart lighting system
is controlled by means of a centralised fuzzy controller. This smart lighting system
is composed of a network of LED lamps which can independently regulate their
illumination level. Each LED lamp has its own control unit which consists of a PID
controller able to generate appropriate control signals as a function of the references
provided by the centralised fuzzy controller. Moreover, this strategy also takes into
account the occupation and the measured luminance level of the room.
Other works make use of ANN, for example Si et al (
2013
), where a RBF neural
network is used to estimate the luminance contribution from each lamp, and a Particle
Swarm Optimisation (PSO) algorithm is used to calculate the optimal dimming ratio
of the lamps, or GA, as Congradac et al. (
2012
) where they were used to regulate
dimming lighting. Finally, the integration of adaptive control approaches to provide
optimal visual comfort conditions under several circumstances is also very common.
For example, in Guillemin and Morel (
2001
) a self-adaptive control system which
alters the priority between visual and thermal comfort as a function of the users'
presence is presented.
5.1.3 Indoor Air Quality Control Strategies
In general, indoor air quality inside a certain environment where the main pollutants
source comes from human activities is controlled by regulating the indoor CO
2
concentration level. Therefore, it is very important to have an appropriate occupation
information to predict the strategies that have to be applied to maintain indoor air
quality in an efficient way. The most used technique is ventilation, both through
windows (natural ventilation) or by means of HVAC systems (forced ventilation).
For example, in Goyal et al. (
2012
) three different control algorithms as a function
of the available occupancy information to efficiently maintain indoor air quality at
zone-level are proposed. The first one makes use of long-horizon accurate occupation
predictions and a dynamic hygrothermal model. The second algorithm is based on
the use of occupation measurements and a dynamic model, and finally, for the third
one, only occupation measurements are necessary. More specifically, the first two
control algorithms are within the framework of MPC and the third one is based on a
classical feedback control architecture.
Furthermore, in literature, different kinds of control strategies can be found:
works based on MPC (Goyal et al.
2012
), those ones based on intelligence engi-
neering, genetic or evolutionary algorithms (Congradac and Kulic
2009
;Kusiak
and Mingyang
2009
; Zhou et al.
1994
) until those ones based on fuzzy logic
(Dounis et al.
2011
).Moreover, it is also possible to find several works which combine
more than one technique, such as Gu et al. (
2008
) where a self-adaptive PID control
