Supervisor of Digital PI-like Fuzzy Logic Controllers for Indoor Lighting Control in Buildings

Supervisor of Digital PI
-
like Fuzzy
Logic Controllers for Indoor
Lighting Control in Buildings


K. Alexandridis

and A. I. Dounis


Department of Automation, Technological Educational Institute of Piraeus,
Piraeus, P. Ralli & Thivon 250, Greece,
Tel: 2105381
338,
email:
aidounis@otenet.gr
.



Abstract

In this paper, we develop
a supervisor of
digital PI
-
like fuzzy logic controllers
(FLC) for indoor lighting conditions control in buildings. The proposed fuzzy
control system has hierarchical structure. This struc
ture consists from one
supervisor and two fuzzy logic controllers. The supervisor evaluates the
daylight and artificial lighting and decides by logic
-
based switching for the
fuzzy controllers’ operation. The structure of a PI
-
like fuzzy logic controller i
s
presented.

The control system is implemented in a simulation environment including
reference models for the building. The environment combines TRNSYS
(Transient System Simulation Program) and MATLAB software’s.
Τ
he role of
the real system is played by a model implemented in TRNSYS. The control
system is implemented in MATLAB. The communication between TRNSYS
and MATLAB is realized by a TRNSYS TYPE that calling the MATLAB Engine
Library.

The simulation results sho
w that the proposed fuzzy control system
successfully manages the
illuminance
comfort and the energy conservation.


Keywords:
Digital Fuzzy Logic Controller, Energy Saving, Lighting

comfort
,
Building, Supervisor.



1. Introduction


The problem of energy s
aving and the achievement of
visual
comfort
conditions in the interior environment of a building is multidimensional.
Scientists from a variety of fields have been working on it for quite a few
decades, but it still remains an open problem. People spend ab
out 80% of
their lives inside buildings. So, achieving lighting comfort conditions in a
building is very important and has direct implication to the productivity of the
occupants and indirect implication to the energy efficiency of the building.

Indoor lig
hting in buildings is a topic of a major importance for researchers
.


Dounis

[4]
proposed
a fuzzy control scheme for visual comfort in a building
zone
.
The indoor illuminance levels together with

the Daylight Glare Index are
taken into account by the fuzz
y

control scheme to regulate the shading and
electric lighting

[7]
.

User behaviours concerning the blind position are often very

complex and
hardly

predictable
.
Guillemin
and Moltemi

[6]

used Genetic Algorithms in a
shading
-
device controller with goal to l
earn the user preferences.

Guillemin
and Morel

[5]

presented a self
-
adaptive multi
-
controller system. In this system
every controller works in order to help the others.

The overall optimization of
the system realised through the use of GA.

In t
he
Lah’s

pa
per

[9,10]

propose
d

a modern

approach to control the inside
illuminance with fully

automated fuzzy system for adjusting shades, which

responds constantly to the changes in the available solar

radiation, which
makes decisions as it follows the human

thinkin
g process.

Th
e main fuzzy
logic controller is linked with
an auxiliary
conventional PID controller
. The goal
of this
lower level
controller is the control the roll position.

Hybrid systems like ANFIS (Adaptive
Neurο
-
Fuzzy Inference System) have
been used for prediction and control of the artificial lighting in buildings,
following the variations of the natural lighting

[
8
].






The
present
paper
presented
a method
supervision control
that uses digital
P
I
-
like FLC
to improve both
lighting level
and energy

efficiency
at the same
time.

The main goal of the proposed
supervisory
control system is

to take full
advantage of daylight for inside lighting.





2.
Considered System


2.1

Simulation environment

(MATLAB
-
TRNSYS)


This environment combines
TRNSYS

16 [16]

and M
ATLAB

software (Fig. 1).
The building model is implemented in TRNSYS and the control system is
implemented in MATLAB. Simulation time step is 6 min. Controllers outputs
belong to interval [0,1
] and
Φ
i is the maximum power of each actuator. The
simulati
on environment, shown in Figure 1
, includes the following
components:

1)

TRNSYS TYPE 56 module
:

Multi
-
zone
Building model
ing
.

2)
TRNSYS TYPE

155:

The interface

between TRNSYS and M
ATLAB
.
The
co
ntrollers are implemented in MATLAB. For the controllers for which an
executable program is available, the file data transfer and the call to
executable routine are also implemented in MATLAB. The TYPE 155 is a
standard TRNSYS routine.

3) T
YPE

9 module
: Th
is component is used to read the weather data files

(TRY

is generated by meteorological data from Athens,

Greece
[1].



4)

T
YPE

16 module
: This component is a radiation processor with smoothing.

5) The
calculations relevant to the natural and artificial i
llumination
,

the
development of the fuzzy controllers
and supervisor
are implemented in the
MATLAB.


All simulations concerned a passive solar building characterized by an
important south
-
facing window glazed area (3m
2
), area 45 m
2
, volume 135 m
3

and by a
high thermal inertia
,
light transmittance of the window glazing

mean

(
τ
=0.817),

reflectance of all indoor surfaces

(
ρ
=0.4)
. In the TRNSYS there
exist an electric lighting (10 lamps, 0
-
1000 lux, 800 W total), and a shading
device (curtain). The controller’s initial set point is: indoor Illuminance= {800
-
600
-
500
-
800}lux.

i


is the maximum power of each actuator.






Φ
i

*
i
u

TYPE

155

Controllers

-

Supervisor

Illuminance

and

CO
2

Calculation

MATLAB

TRNSYS

TYPE 56

(Actuators)

TYPE
16

TYPE 9



Fig
ure 1
:

Simulation block diagram.

2.2
Lighting


Indoor
Natural Lighting


The average indoor illuminance Ε
in

(lx)
[11]
is calculated usi
ng the equation

)
1
(




in
v
w
in
A
E
A
E


(
1
)

where

A
w

(m
2
) the window surface

τ (
-
) the light transmittance of the window glazing

Ε
v

(lx) the vertical illuminance on the window

A
in

(m
2
) the total area of all indoor surfaces

ρ (
-
) the area weighted mean reflectance of all indoor surfaces.

The vertical ill
uminance on the window Ε
v

(lx) is given by the following
equation

v
G
v
G
k
E


(2
)

with

k
G

(lm.W
-
1
) the luminous efficacy of global solar radiation

G
v

(W.m
-
2
) the global solar radiation on the window surface


The luminous efficacy of global so
lar radiation
[13]
can be calculated by the
following relation

s
h
h
D
h
h
G
k
G
D
k
G
D
k











1


(3
)

with

D
h

(W.m
-
2
) the diffuse horizontal solar radiation

G
h

(W.m
-
2
) the global horizontal solar radiation

k
D

(lm.W
-
1
) the luminous efficacy of diffuse solar radiat
ion

k
S

(lm.W
-
1
) the luminous efficacy of beam solar radiation.


The luminous efficacy of diffuse solar radiation
[12]
is calculated using the
equation

C
k
D
29
144



(4
)


3
2
68
.
1
22
.
1
55
.
0
1
NI
NI
NI
C





(5
)


)
(
sin
12037
.
0
1
1
82
.
0
z
h
h
G
D
NI







(6
)

with θ
z
(deg) the solar zenith angle.


Finally, the luminous efficacy of the beam solar radiation
[2]
can be calculated

using the relat
ion


k
S

= 17.72 + 4.4585 θ
z

–
=
8KT5SP=

㄰
-
2

(θ
z
)
2

+ 7.3948


㄰
-
4

(θ
z
)
3

–
=
2KNST=


㄰
-
6

(θ
z
)
4

–
=
8K4NP2=


㄰
-
10

(θ
z
)
5

(7
)


A qualitative criterion for the control performance is the value of Illiminance
Discomfort Index (IDI) (Equation 8).
K
is the index
of the sample,
T
i

the
sampling time and
e
i

the sample error.



i
i
i
( e T)
1
T
1
k
i
I I
k
i






D


(8
)


Artificial Lighting

The
Equat
ion below is used to calculate the average artificial light intensity
inside the buildings:

*
( )
2
2 ( )
AL
AL
u N P V n
H h


   

 


(
9
)

where

*
AL
u
: The actuating signal of the artificial light controller, ranging from 0
-
1. This
signal is driven by

the artificial lighting fuzzy controller. The same signal is
also fed into the building model (Archimed.bui) to drive the actuator for the
artificial lights.
If
*
0
AL
u


means that all lights are off
.

If
*
1
AL
u


mean
s that all
lights are on at full power. In the latter case, the equivalent intensity is
approximately
E
AL
=1000 lux.
N: Number of light
lamps

(N=10)
,
P: The power
per lamb
(
P=
60W)
,
V: The luminous efficacy/efficiency of each
lamp

(
V=
60
lumen/W)
,
n: The powe
r efficiency of each
lamp
(
n=
0.7)
,
H:
The
height lamp
from floor

(H=3m)
,
h: The
height
reference working level, measured from the
floor (h=1m).


3.
Digital PI
-
like

FLC


The proposed PI
-
like FLC is useful because in the building control systems
there are ac
tuators with continuous output such as variable speed fans, hot
water heating systems, electrical heaters, air inlets.
All the membership
functions of the PI
-
like FLC inputs/outputs are shown in
Figure
s

3

and
4.

The
input/output normalization maps the stat
e variables on the interval [
-
1,+1]. The
scaling factors
are chosen to be
e
G
=
1/
1200
,

e
G

=
1/
120000

and
u
G

=1
. These
scaling factors have been found via simulations (trial and error).

T
he output of
each controller is
( ) {,}
AL SH
u k u u

The fuzzy control rules

are presented in
Table 1 and 2.


Figure 2:
Digital implementation of a PI
-
like FLC




Figure 3
:
Membership functions

of the
FLCs output variables
.





Figure 4
:
Membership functions of the
FLCs input variables
.



Δ
e


e

NB

NM

NS

ZE

PS

PM

PB

NB

PB

PB

PB

PB

PM

PS

ZE

NM

PB

PB

PB

PM

PS

ZE

NS

NS

PB

PB

PM

PS

ZE

NS

NM

ZE

PB

PM

PS

ZE

NS

NM

NB

PS

PM

PS

ZE

NS

NM

NB

NB

PM

PS

ZE

NS

NM

NB

NB

NB

PB

ZE

NS

NM

NB

NB

NB

NB

Table 1:
The control rules of the fuzzy controller of the shading

(
Δ
u
SH
)

-
1

-
0,5

1

0,5

0


1

0,8


0

0,6

0,4

0,2

Δ
u
SH
, Δ
u
AL

NB

PB

Membership function

-
0,25

0,25

NM

NS

ZE

PS

PM

( 1)
u k


( )
u k


( )
u k

( 1)
e k


( )
e k


( )
e k

( )
r k

u
G


e
G

e
G


Σ

+

-

Σ

+

-

1
z


Fuzzy
PI

Σ

+

-

( )
y k

TRNSYS

1
z


0

-
0.5

-
1

0.5

+1

e,
Δ
e

Z

E

NS

NM

NB

PS

PM

PB

Δ
e


e

NB

NM

NS

ZE

PS

PM

PB

NB

N
B

N
B

N
B

N
B

N
M

N
S

ZE

NM

N
B

N
B

N
B

N
M

N
S

ZE

P
S

NS

N
B

N
B

N
M

N
S

ZE

P
S

P
M

ZE

N
B

N
M

N
S

ZE

P
S

P
M

P
B

PS

N
M

N
S

ZE

P
S

P
M

P
B

P
B

PM

N
S

ZE

P
S

P
M

P
B

P
B

P
B

PB

ZE

P
S

P
M

P
B

P
B

P
B

P
B

Table 2:
The control rules of the fuzzy controller of the artificial lighting

(
Δ
u
AL
)


4.
Supervisor Architecture


The proposed control system can be ref
erred to as intelligent control system
because the actions of the controller attempt to mimic high level decision
making processes of human operators.

The architecture of supervisor unit is
shown in Figure 5 and the supervisor logic is presented in Table 3
.



Table 3
:
Supervisor (logic
-
based switching)



e


PI
–
FLC
(AL)



AL
u

1
a

*
AL
u

e r y
 

e


PI
–
FLC
(SH)



SH
u

2
a

*
SH
u

TRNSYS

Supervisor

Calculation of i
ndoor natural
illuminance without shading

(Equation 1)

Zone illuminance (y)

I
lluminance
desired

(r)

e r y
 

I
f
Illum
inance

desired
(r
(k)
)

Nat
ural
Illum
inance without shading
(k)

Then

α
1
=
0 →
*
( ) 0
AL
u k


α
2
=
1 →
*
( ) ( )
SH SH
u k u k


Else

α
1
=
1 →
*
( ) ( )
AL AL
u k u k


α
2
=
0 →
*
( ) 0
SH
u k


end

Supervisor

Figure
5
:
The architecture of proposed control system

5
. Simulation Results

The

performance

of

the two

controllers

is

summarized

in

T
able

2
.
The

perfo
rmance

criteria

are

the

response

performance, the illuminance
discomfort index, the natural lighting exploitation and the energy consumption
for electric lighting.

The

energy

consumption

is

calculated

for the one day
simulation period.
In Figures 6 and 7
give the response performance of indoor
illuminance with and without complete exploitation of natural lighting.
The
response of system output successfully approaches the set points.
In the case
1 the

control

system

involves important energy saving about 90
%

concerning
case 2
.
However
,
in

the

case

2

the

control

system

does not achieve a low IDI
since the daylighting is one of the main reason’s that cause glare and visual
discomfort in occupants
.



Figure 6
:
Response performance of
Indoor

illuminance

without

the
complete

exploitation

of natural lightting (
15
η
=
April)
K



Figure

7
:

Response performance of

Indoor

illuminance

under

the

complete

exploitation

of natural lightting (
15
η
=
April)
K
=


Performance of the zone level controllers

(Illuminace tolerance=50lux)

Performance

without the complete
exploitation of

natural lighting

(Case 1)

Performance

under
the complete
exploitation of
natural light
ing

(Case 2)

I
D
I
=21
.
865 lux

I
D
I
=27.
570 lux

Natural lighting exploitation
=56%

Natural lighting exploitation

=94%

Response Performance

O
vershooting
: approximately zero

Steady state error: approximately zero

Response Performance

Overshooting: about 32%

Steady state error: approximately zero

Energy consumption

(
KWh/m
2
)


A
rtificial
l
ighting
=
31


10
-
3


Motor for shading
=
1
,
2
6
6

10
-
6


Energy consumption (
KWh/m
2
)


A
rtificial lighting
=2,9

10
-
3


Motor for shading
=
2
,
055

10
-
6


Table 4
:
The

performance

without

and

with

without the complete exploitation
of
natural light
ing

6
. Conclusions

In this
paper, we develop
a supervisor of
digital PI
-
like fuzzy logic controllers
for indoor lighting conditions control in buildings.

The simulation results show
that the proposed fuzzy control system
is achieved illuminance
comfort and
important
energy

saving
s
.
The supervisory control
system achieves

energy
saving
based on the
full

exploitation of the
daylight
.


Acknowledgements

The project is co
-
funded by the European Social Fund & National Resources
-

EPEAEK II
–

ARCHIMIDIS.


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