CHAPTER 5 ×× ANFIS LIGHT AND FUZZY LOGIC WINDOW BLIND …shodhganga.inflibnet.ac.in/bitstream/10603/4977/11/11... · 2015-12-04 · fuzzy based functional inverse. To obtain the

Adaptive Predictive Lighting Controllers for Daylight Artificial Light Integrated Schemes

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CHAPTER 5××××

ANFIS LIGHT AND FUZZY LOGIC WINDOW BLIND CONTROLLER

5.1 Introduction to Implementation Details

Automatic controllers for electric lighting have been one of the mainstays of the effort to use

daylighting in order to reduce annual lighting energy consumption. Currently, available

commercial daylight responsive lighting controllers employ a photosensor technology

teamed with dimmable fluorescent lighting system reduces which energy demand by

dimming lights proportionally to the amount of daylight received at a reference plane.

However, most of these controllers do not work satisfactorily due to the high non-linear

nature of daylight availability and penetration into the interior [7]. In this perspective,

general notions and important impediments to the deployment of existing automatic

photosensor based light controllers in buildings has already been dealt with in Chapter 1.

The above stated reasons call for a design of electric lighting controllers that adapt to

variation in ambient daylighting conditions. There have been many propositions and efforts

to employ artificial intelligence techniques as an efficient tool to redress the shortfalls of

current conventional lighting controllers. For instance, in the frame work of international

energy agency projects EDIFICIO and research work [29, 43] have proved SCT to be a

promising technology with respect to the complex integrated and adaptive control problems

encountered in the building sector. An overview of technologies, tools and earlier researches

to bridge the gap and form the control strategy has been discussed earlier in Chapter 2. The

present chapter provides a succinct conceptual background of self adaptive predictive

scheme for the integrated light and window blind control strategy. Emphasis is also laid on

The control scheme presented in this chapter has been awarded Intellectual Ventures Invention Award 2011.


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Fig. 5.1 Simplified functional block diagram illustrating the control scheme of integrated ANFIS light and fuzzy logic window blind controller.

-

+ Reference

Illumination

OPTICAL PROCESS

Actuators:

Light dimming

Blind positioning

Interior

Illuminance

Disturbance Solar Illuminance

Occupancy User wishes

PC based

Control algorithm

PC CONTROL PANEL:

Supervision & Visualization

TEST CHAMBER

PC Control algorithm

Etotal

Blind up/down &

Slat angle position

Controlled variable: Interior illuminance

+

(Eelectric

Edaylight

+

Set point illuminance

Edesired +

Emeasured - Dimming

signal

ANFIS

controller

Process:

Elec. Lighting

Light sensor

Window

Blinds

Disturbance: Ext. Solar illuminance User wishes Occupancy

Fuzzy

controller

the presentation of real time implementation and validation of developed controller in a

daylight-artificial light integrated scheme. To obtain robust controller performance, the

parameters of the proposed controller is updated online to minimize the control error. The

idea is to design a control approach that optimizes energy consumption and occupant visual

comfort. In the present context, visual comfort points to the daylight glare control and

gratification of user defined interior illuminance level with high illuminance uniformity

ratio.


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In accordance with the research objective defined in the thesis, Fig. 5.1 shows the

block diagram of prototype controller scheme that utilizes real-time sensing techniques. The

current scheme is a novel computational model based on SCT for artificial light and window

blind control for the interior illuminance regulation to set point level. The prototype control

system designed entails computer based soft computing algorithms. These intelligent

algorithms facilitate real time integration as well as the control of artificial lights and

window blinds in a daylight plus occupancy dependent dimming applications. The adaptive

predictive scheme embraces: (a) an online ANFIS interior daylight illuminance predictor in

conjunction with ANFIS inverse artificial light intensity control algorithm for the regulation

of interior illuminance, and (b) fuzzy logic based window blind control algorithm to prevent

occupants from glare.

In the present work an experimental setup is realized; a prototype design is

developed and tested in real time. The design concept has already been depicted in full and

in detail in Figure 1.3 of Chapter 1.The following subsections enumerates:

(a) online ANFIS inverse control for artificial light intensity.

(b) fuzzy logic based window blind controller.

The real time implementation of the scheme with particular emphasis to assess energy

efficiency and visual comfort is subsequently discussed.

5.2 Conceptual Scheme of Online ANFIS Controller for Artificial Light Control

The developed ANFIS controller whose structure is shown in Fig. 5.2 is composed of an

online process identifier plus a five step ahead interior illuminance predictor concatenated

with ANFIS inverse based artificial light control signal generator. This closed loop approach


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Fig. 5.2 Block diagram depicting online ANFIS process identification, prediction and control.

ANFIS Process

Identifier,Predictor

and Controller

)(ky

Online process identification

Online parameter

adaptation

)(kE )(ku )( pkE +

error

)(ˆ ku

ANFIS Inverse Model

ANFIS Process Model

PZ

−

ANFIS Control

Real time Process

achieves its goal by utilizing a prediction of the system output based on the internal model

of the process. The ANFIS modelling approach described here serves two objectives:

(a) It provides a basic model for illuminance system identification that depicts the behaviour

of the underlying system.

(b) Applies ANFIS inverse model for controller design.

The plant identifier is very crucial for the successful estimation and tuning of

adaptive controller parameters. The particulars of the online plant estimator and predictor

models have been discussed in the previous Chapter 4. As stated previously, ANFIS

artificial light controller attempts to maintain a constant desired illuminance on the working

plane. As a result, daylight represents perturbation signal for the controller. The artificial

light intensity control represents a process whose mathematical model is unknown.

Therefore, the applied control structure necessitates inverse modelling of the process. In

relevance to the design of artificial lighting controller, the next section provides a succinct

and specific explanation of inverse control theory.


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(a)

yd(k+1)

y(k)

u(k)

y(k+p)

Plant

ANFIS ANFIS

Controller

(b)

Fig. 5.3 Block diagram of the inverse control method: (a) Training phase, (b) Application phase

y(k+p)

u(k)

ε(k)

û(k)

y(k)

ANFIS inverse model

Plant ANFIS

5.2.1 Theory of Inverse Control

Inverse System theory is an effective tool for general nonlinear system control in finding the

inverse of the system. Briefly, the identification of the inverse dynamics model is to

calculate the input variables according to the output process of the system. According to the

inverse system theory, the inputs of a system can be inverted by analyzing the historic inputs

and output sequence. To model this inverting process, an inverse dynamics model of the

system can be obtained. In this section discrete-time feedback control system discussed in

the literature [124] is applied to design ANFIS inverse light intensity control for a daylight-

artificial light integrated scheme. Assuming that the state variables are measurable and

known in number, for a first order plant:

( ) ( ) ( )( )kukyfpky ,=+ (5.1)

Equation 5.1 denotes that with u as the input, the state of the first order plant in question

will move from )(ky to ( )pky + in p time steps. For obtaining the inverse model, assumption

is made that the inverse dynamics of the plant exist, and hence u can be described as an

explicit function of )(ky and ( )pky + as:

( ) ( )( )pkykyGpku +=+ ,)( ( 5.2)


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u(k)

error

û(k)

E(k+p)

E(k)

ANFIS inverse model

Fig. 5.4 Structural block diagram representation of ANFIS inverse light controller.

The assumptions of the existence of inverse dynamics essentially mean that there is a unique

control sequence u as specified by G that can drive the plant from )(ky to ( )pky + exactly in

p time step.

The first phase of the inverse learning is called the ‘learning phase’ and is illustrated

in the block diagram of Fig. 5.3(a). Here, the plant output ( )pky + is a function of previous

state of )(ky and )(ku in accordance with Eq. (5.1). With the adaptive network being able to

capture the input-output mapping of the inverse dynamics, a second phase follows. This

second phase is known as ‘application phase’ whose block diagram is shown in Fig. 5.3(b).

During the application phase, the network generates an estimated control sequence which is

a function of previous state )(ky and ( )pkyd + . The control sequence would

bring )(ky to ( )̀pkyd + , if f is exactly the same as function G . As the training process

advances, parameters in the network get refined and function f becomes closer to G . As a

result, the control process becomes more robust and adaptive. Next subsection attempts to

describe the scheme for ANFIS inverse light control.

5.2.2 ANFIS Inverse controller for artificial light intensity control−−−−Implementation


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Table 5.1 Switching and Dimming Control Scheme for designing ANFIS light controller.

Relation between Etask and Es Control Signal Artificial light condition

sEEE tasks <<2.0

s

task

E

E−1

30% to 100% of light output

stask EE > Zero All OFF

stask EE 2.0< 1 All ON

To achieve a nonlinear controller, ANFIS system is trained to learn the inverse

dynamics of artificial light process. A control scenario as given in Table 5.1 is hypothesized

to realize a nonlinear relationship between the collected interior illuminance data with the

control signals. Assuming that inverse of the process exists, define illuminance data set

acquired from sensors as )(ky and the dimming control signal for the EDB as )(ku to

establish the inverse mapping f . Figure 5.4 depicts the structure of the proposed neuro-

fuzzy based functional inverse. To obtain the inverse models, control signals to the dimming

ballast can be articulated as an explicit function of the past, present and next step ambient

illuminance values E . The inverse model has two inputs )(kE and )6( +kE and the output )(ku

is the control action to the EDB as given by Eq. (5.3).

( ))(),5()5( kEkEGku +=+ (5.3)

For the proposed online application, to deal with the time varying illuminance

dynamics, the control actions in Eq. (5.3) are generated every five minutes based on the five

step ahead interior illuminance predictor output. Accordingly, ANFIS inverse controller

triggers control signal to the dimming ballast ranging within 30% to 100% to account for

ambient daylight variation within 100% to 20% of the set level as illustrated in Table 5.1. If

the available daylight illuminance taskE goes above sE , the corresponding control signal will


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be 0 and the lights will be fully off. If taskE becomes lesser than sE2.0 , the lamps are fully

bright with a control signal value of 1. In between this boundary, the control signal is

generated by the formula ( )[ ]stask EE /1− , which enables the entire space to have a desired

illuminance profile as specified by the user at any time instant k .

Fig. 5.5 Structure of implemented ANFIS model.

ANFIS System : 2 inputs, 1 outputs, 9 rules

input1 (3)

input2 (3)

f(u)

output (9)

anfis

(sugeno)

9 rules

Fig. 5.7 Error curves and step sizes for the designed ANFIS inverse model.

0 10 20 30 400.01

0.012

0.014

0.016

0.018

0.02

Epoch Number

Ste

p S

ize

Training Data

Test Data

0 10 20 30 400.01

0.02

0.03

0.04

0.05

0.06

Epoch Number

RM

SE

Fig. 5.6 (a) Initial membership functions before ANFIS training, (b) final membership functions after ANFIS training.

-1 -0.5 0 0.5 10

0.2

0.4

0.6

0.8

1

Input 1:E(K)

Deg

ree o

f M

em

bers

hip

-1 -0.5 0 0.5 10

0.2

0.4

0.6

0.8

1

Input 2:E(K+6)

De

gre

e o

f M

em

be

rsh

ip

-1 -0.5 0 0.5 10

0.2

0.4

0.6

0.8

1

Input 1:E(K)

De

gre

e o

f M

em

be

rsh

ip

-1 -0.5 0 0.5 10

0.2

0.4

0.6

0.8

1

Input 2:E(K+6)

De

gre

e o

f M

em

be

rsh

ip

Input 2: E(K+5) Input 2: E(K+5)


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The structure of the ANFIS model is presented in Fig. 5.5. An ANFIS with three g-bell

membership are used for training and identification of process dynamics. The trained ANFIS

model therefore has a total of 9 fuzzy rules with a total of 35 fitting parameters of which 16

are premise parameters and 24 are consequent parameters. The initial and the final

membership functions before and after model training are depicted in Fig. 5.6(a) and 5.6(b)

respectively. These membership functions portray the input with a varying degree of

membership, for instance low, medium, and high illuminance levels. For the design of the

fuzzy controllers, grid partitioning method is employed. The root mean squared error

(RMSE) and step size graph of the model are displayed in Fig.5.7. It can be observed that at

the end of 15 epochs, the RMSE of both the training and test data set is approximately

0.014. This negligible small RMSE indicates that ANFIS has captured the essential

behaviour of underlying system. Additionally, very close values of RMSE for both test and

training data set indicate that the model exhibits a good generalization capability. As

illustrated in Fig. 5.7, model learning is almost complete in the initial 14 epochs training and

therefore model training is stopped at 40 epochs. Moreover, after epoch number 38, the step

size reduces to 0.008.

5.3 Fuzzy Logic Based Window Blind Control

Shading systems like window Venetian blinds are beneficial particularly in the tropics and in

the summer season, to restrict solar heat gain and glare from direct sunlight. Altering blind

slat angle allows adjustment of daylight level and distribution in interior space and

controlling of glare condition due to excessive illuminance on window [30]. In the current

study, fuzzy logic based window blind control algorithm is developed taking into account


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the tropical climate of South India. The objective is to evade discomfort daylight glare

penetrating into the interiors by automating variation of slat angle tilt and up-down

movements of the Venetian blinds. The transmittances of the window blinds are presented in

the Appendix-A7. Seeing that the control variables are continuous and simultaneous human

interactions involved, fuzzy logic is considered as a good solution to this conceptual scheme.

Human interactions are considered in the form of occupancy as well as user preferences.

Exterior vertical illuminance on window provides timely information and the best indication

of glare [31]. In the present work daylight on window in terms of vertical global and diffuse

illuminance are selected as the main fuzzy input for the window blind controller, making it

distinctive from other models that were previously discussed in section 2.3.1 of Chapter 2.

Real time exterior daylight sensor data is employed to estimate global and diffuse

Illuminance. The discomfort daylight glare is experimentally judged in terms of sky ratio.

Sky ratio is the ratio between the exterior diffuse illuminance to the exterior global

illuminance. It is experimentally found in this work that SR<0.3 and 0.3<SR<0.6 causes

discomfort daylight glare. Especially, for SR<0.3 sun is at low altitudes shining directly in

front of window. On the contrary, SR>0.6 creates a sensation of diffuse daylight entering

the interiors. As depicted in Table 5.2, the designed automatic motorized Venetian blind

system algorithm lowers the blind and adjusts the blind slat angles in the presence of glare.

But, during the non glare period, the window blinds are raised up making way to admit

diffuse daylight into the interiors. This unique design model based on sky ratio concept

eliminates unnecessary window blind movements to evade glare. It is observed during

experiments in clear sky condition that SR < 0.6 approximately prevails from 7:30a.m to

11:30a.m for east facing window and after 1:00p.m to 5:30p.m for west facing window


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respectively. North facing window always receive diffuse daylight through out the day and

hence at all times SR>0.6. During clear sky condition, however for a south facing window

SR<0.6 persists from morning 7:30a.m to evening 5:30p.m induce discomfort glare. Figure

5.10(a) shows the surface plot of variation of blind slat angles in relation to the exterior

illuminance for a south facing window.

5.3.1 Implementation of fuzzy logic window blind controller

Table 5. 2 Window blind control scheme for designing fuzzy logic window blind control.

In the present work, the role of fuzzy control algorithm is to produce appropriate signals for

Venetian blind slat angle positioning so as to eliminate daylight glare. The primary reason

for choosing the fuzzy logic controller is: (a) the control strategy can be represented by a set

of rules, and (b) controller doesn’t essentially require the exact set of equations to represent

Sky Ratio (SR)

Glare Assessment

Blind Slat angle Blind Up/Down Position

SR≤0.3 Glare 90o (slats fully closed)

Blind slats are completely closed and fully rolled down to completely cover the window in order to eliminate the daylight glare.

0.3<SR<0.6 Glare

45o (slats half open)

Blind slats are open at 45o but, fully rolled down to completely cover the window in order to eliminate the daylight glare.

SR≥0.6 No Glare 0o(slats full open) Blind slats are open at 0o and fully rolled up in order to uncover the window for daylight admittance.

Exterior

45o

Interior

Exterior

90o

Interior

Exterior

0o 0o= 180o Interior

window


100

the system. These aforementioned reasons allow the design to change the basic

characteristics of the controller with minimal effort i.e., simply by redefining the rules. The

present section depicts the application phase of fuzzy logic controller and theoretical

background is implied wherever essential.

Figure 5.8 shows the internal structure of fuzzy logic controller and its method of

application to control the window blind. As mentioned previously, daylight glare depends on

the exterior vertical illuminance. For this reason, as depicted in Fig 5.9 a unique rule base is

Fig.5.8 Block diagram for fuzzy logic window blind controller.

Eg(Klx) (4)

Ed(Klx) (4)

BlindPosition (3)

(mamdani)

11 rules

0 10 20 30 40 50 60 70 80 90

0

0.2

0.4

0.6

0.8

1

Input Variable:Eg(Klx)

Degre

e o

f m

em

bers

hip

(µ)

VL M HL

0 10 20 30 40 50

0

0.2

0.4

0.6

0.8

1

Input Variable:Ed(Klx)

Deg

ree

of

me

mb

ers

hip

( µµ µµ) VL M HL

0 10 20 30 40 50 60 70 80 90

0

0.2

0.4

0.6

0.8

1

Output Variable:BlindPosition

De

gre

e o

f m

em

bers

hip

( µµ µµ)

L(BlindUp) H(BlindDown)M(BlindDown)

Fig.5.9 Input and output membership functions and linguistic variables.

(a) (b)

Fig.5.10 (a) Surface plot of variation of blind slat angle (deg) in relation to variation of exterior global and diffuse illuminance, (b) blind slat angle (deg) variation profile with SR.

Fuzzy Logic Controller

Eg

Ed

Control signal for window blind slat angle

adjustment and up/down

movements

Fuzzification Defuzzification

Decision making


101

formed for the process with the input fuzzy variables as gE , dE respectively and window

blind position as the output variable.

5.3.1.1 Fuzzy membership functions

As depicted in Fig. 5.8, the process is described by a fuzzy system. The first step in

the fuzzy controller design involves specifying the control inputs as well as output variables

and their domains. The numeric input variable measurements are transformed by

fuzzification part into the fuzzy linguistic variable. The present design utilizes triangular

membership functions for both the input and output linguistic variables. In the antecedent

part, the input variables are divided into four steps and in the consequent part into three

steps. The fuzzy membership function associated with the linguistic fuzzy variables is

shown in Fig. 5.9. Table 5.3 shows the linguistic labels associated fuzzy membership

functions.

Table 5.3 Linguistic labels for fuzzy membership functions. Linguistic Label Description

VL Very Low L Low

M Medium H High

5.3.1.2 Decision Making

On the basis of the preliminary experiments and the observations in the test chamber,

a set of linguistic rules is designed. Since dE is never greater than gE only eleven rules could

be formulated with each input variables assigned to the input linguistic variables. These

rules are framed with a simple IF-THEN structure based on the Table 5.4. The premise (IF-

part) describes a certain situation in the form of a fuzzy specification of measured exterior

global and diffuse illuminance values. The conclusion (THEN-part) specifies an appropriate

fuzzy output in terms of blind slat angle position. In the IF-THEN rules, the fuzzy subsets


102

and set are combined with the logical fuzzy operations such as AND, OR and NOT. In short,

the rule base is represented by Fuzzy Associative Memory (FAM) table as shown by Table

5.4. The fuzzy logic controller discussed in this work incorporates Mamdani’s Min

operation as an inference method. This implication has a simple min-max structure

encompassing two phases of operations. In the first phase, the two input variables are

involved with min-operation, hence the antecedent pair in the rule structure are constructed

by logical AND. Then all the rules are aggregated by using max operation.

Table 5.4 FAM table for window blind salt angle positioning Ed Eg

VL L M H

VL H1 - - - L L2 L3 L4 - M L5 M6 H7 -

H L8 L9 L10 M11

- represents Nil; since always dE < gE

5.3.1.3 Defuzzification

Defuzzification is the process for transforming the fuzzy control statement into

specific control actions. Actual slat angle is determined by the proportion of defuzzified

value. The PC generated digital control output from the fuzzy logic controller of PC is

transmitted to DAQ to activate the window blind motor driver relays. Figure 5.10(b)

portrays blind slat angle variation profile with sky ratio. Referring to Table 5.2, in order to

position the blind slat angles at either 0o or 45o or 90o, a digital value ranging from

[0:250:500] from the controller drives the blind slat angle positioning motor. Accordingly, a

10 K, 10 turn rotational potentiometer [125] measures and feeds back the blind slat angle

value to PC for the comparison process. Additionally, a digital value ranging from [0:10000]

lowers or raises the blind to cut-off or admit daylight into the interior. In the present work,

the operation of raising or lowering the blind completely requires approximately 10 seconds


103

(d)

Fig. 5.11 Integrated real time performance of ANFIS light and fuzzy logic window blind controller depicted for south facing window with continuous data monitoring on 11/12/2010 and 12/12/2010. (a) Exterior vertical global and diffuse illuminance profile (b) Performance profile of FL window blind controller illustrating window blind slat angle variation to eliminate glare. In Zone1 and Zone2 profile of: (c) Interior unregulated and regulated illuminance d) Performance of ANFIS light controller depicting percentage dimming control voltage generated to EDB.

(c)

0 500 1000 1500 2000 25000

10

20

30

40

50

60

70

80

90

100

110

Time Sample Distance(min.)

Bli

nd

Til

t an

gle

(d

eg

)

(a) (b)

0 500 1000 1500 2000 25000

10

20

30

40

50

60

Time Sample Distance(min.) starting from 00:00 A.M

Exte

rio

r vert

ical

Dayli

gh

t Il

lum

inan

ce (

Klx

)

Measured Global Illuminance

Measured Diffuse Illuminance

0 500 1000 1500 2000 25000

100

200

300

400

500

600

700


Inte

rio

r il

lum

ina

nc

e (

lx)

Zone 1

Predicted Daylight Illuminance

Controlled Illuminance

Reference Illuminance

0 500 1000 1500 2000 25000

100

200

300

400

500

600

700

800


Inte

rio

r il

lum

ina

nc

e (

lx)

Zone 2




0 500 1000 1500 2000 25000

20

40

60

80

100


% D

imm

ing

Sig

na

l C

on

tro

l V

olt

ag

e

Zone 1

0 500 1000 1500 2000 25000

20

40

60

80

100


% D

imm

ing

Sig

na

l C

on

tro

l V

olt

ag

e

Zone 2

of time. In order to carry out this operation, a delay of 10000 milli seconds is provided in the

microcontroller corresponding to a digital control value of 10000.

5.4 Implementation Results of ANFIS Light and Fuzzy Logic Window Blind Controller


104

The proof-of-concept trials are performed in a dedicated daylighting laboratory as

previously detailed in Chapter 3. This section illustrates the validation of ANFIS lighting

and fuzzy logic window blind controller respectively in an attempt to regulate set point task

level illuminance and avoid daylight glare. The overall system is implemented in MATLAB

environment. In this way, its state can be monitored on a PC and various control scenarios

can be directly examined as depicted in Fig.3.8 of Chapter 3. A real time validation process

for checking the prototype controller performance is performed from 2nd October 2010 to

10th November 2010. For elucidation, first consider the results presented for a south facing

window for a typical office working hours from 8am to 5pm (local time) Monday to

Saturday. Figure 5.11 illustrates the system functionality in two zones of the test chamber on

December 11th and 12th, 2010. For validation, the task illuminance levels are determined at

two reference positions (point 5 and point 7) in each lighting zone at a work plane height of

0.7m above the floor centered with respect to the window as shown in Fig. 3.2(c) of Chapter

3. For the purpose of this discussion, this thesis considers the target minimum average

illuminance on the task area is 500lx, as is consistent with many lighting standards [98]. For

demonstrative purposes, the dimming control operates every five minutes to regulate the set

point task illuminance. Furthermore, the controller switches off artificial lights incase space

becomes unoccupied for more than ten minutes commencing from last presence detection by

the occupancy sensor. In response to the real time exterior illuminance portrayed in Fig

5.11(a) the proposed fuzzy logic based automatic motorized Venetian blind system

algorithm lowers the blinds and adjusts the blind slat angles in the presence of glare and

raises the blind during non glare as depicted in Fig 5.11(b). Figures 5.11(c) and 5.11(d)

presents the interior illuminance variation in the two zones respectively. Moreover, Figs.


105

5.11(c) and 5.11(d) shows how ANFIS control system attempts to preserve 500 lx on the

task area by supplementing artificial light with deficit interior daylight. If the interior

daylight is lesser than the target illuminance of 500 lx, ANFIS controller tops up the

artificial lighting to meet user defined illuminance on the work plane. Referring to Figure

5.11(d), the controller independently actuates the actuators in the two zones to turn on the

lights when the daylight illuminance is lesser than 450lx on the task. Likewise, lights are

turned off when the daylight illuminance exceeds 550lx on the task. Hence, the consequent

parameters of ANFIS controller are updated online by RLS algorithm if the system tracking

error exceeds ±10% of set point illuminance. In the two zones, every five minute, the sensor

values are fed back and compared to the target values. Incase the feedback value exceeds the

target level, no control signal is generated and lights are switched off. Or else, the controller

triggers an analog control signal voltage through PC DAQ to dimming ballast in order to

regulate the set point illuminance. In the control algorithm, priority is set to create an

optimum illuminance condition at the task surface. During the control interval, the ANFIS

controller transmits an eight bit digital value in the range of [0:255] from PC to DAQ,

corresponding to 0% to 100% dc control voltage to EDB. Figures 5.12 to 5.14 illustrate the

performance of the scheme for other orientations of the window namely: west, east and

north respectively.


106

0 500 1000 1500 2000 25000

200

400

600

800

1000


Inte

rio

r il

lum

ina

nc

e (

lx)

Zone 1




0 500 1000 1500 2000 25000

200

400

600

800

1000


Inte

rio

r il

lum

ina

nc

e (

lx)

Zone 2




0 500 1000 1500 2000 25000

20

40

60

80

100


% D

imm

ing

Sig

na

l C

on

tro

l V

olt

ag

e

Zone 1

0 500 1000 1500 2000 25000

20

40

60

80

100


% D

imm

ing

Sig

na

l C

on

tro

l V

olt

ag

e

Zone 2

0 100 200 300 400 500 600 700 800 9000

10

20

30

40

50

60

70

80

90

100

110


Blin

d T

ilt

an

gle

(d

eg

)

0 100 200 300 400 500 600 700 800 9000

10

20

30

40

50

60

70

80

90

100


Exte

rio

r vert

ical

Dayli

gh

t Il

lum

inan

ce (

Klx

)



Fig. 5.12 Integrated real time performance of ANFIS light and fuzzy logic window blind controller depicted for east facing window with continuous data monitoring on 03/01/2011 and 04/01/2011. (a) Exterior vertical global and diffuse illuminance profile (b) Performance profile of FL window blind controller illustrating window blind slat angle variation to eliminate glare. In Zone1 and Zone2 profile of: (c) Interior unregulated and regulated illuminance d) Performance of ANFIS light controller depicting percentage dimming control voltage generated to EDB.

(a) (b)

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Fig. 5.13 Integrated real time performance of ANFIS light and fuzzy logic window blind controller depicted for west facing window with continuous data monitoring on 10/01/2011 and 11/01/2011. (a) Exterior vertical global and diffuse illuminance profile (b) Performance profile of FL window blind controller illustrating window blind slat angle variation to eliminate glare. In Zone1 and Zone2 profile of: (c) Interior unregulated and regulated illuminance d) Performance of ANFIS light controller depicting percentage dimming control voltage generated to EDB.

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The total electricity consumption includes rated energy consumption by two

luminaires employed in two lighting zone of the test chamber. Hence, the base case is

represented by lighting power consumption at rated 144 W without dimming controls.

Figure 5.12 shows the bar graph representation of percentage mean monthly energy savings

Fig. 5.14 Integrated real time performance of ANFIS light and FL window blind controller depicted for north facing window with continuous data monitoring on 15/01/2011 and 16/01/2011. (a) Exterior vertical global and diffuse illuminance profile (b) Performance profile of FL window blind controller illustrating window blind slat angle variation to eliminate glare. In Zone1 and Zone2 profile of: (c) Interior unregulated and regulated illuminance d) Performance of ANFIS light controller depicting percentage dimming control voltage generated to EDB.

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from November 25th, 2010 to February 25th, 2011 for maintaining the desired illuminance

level of 500lx for each window orientation respectively. It is to be noted that all the

measurements are carried out with any one window open at a time with the remaining three

windows closed. The analysis is provided during office working hours for a period of four

months from 8:00a.m to 5:00p.m (local time). Referring to Table 5.5, the experimental

result with the proposed controller indicate approximately 30% to 42% lighting energy

saving can be attained with good visual uniformity above 0.8. Nonetheless, these figures

can be regarded as an indication of likely energy savings from the proposed light control

algorithm. Additionally, the energy savings are mainly attributed to the typical tropical

climate of South India, occupancy dependent lamp switching as well as efficient lamp and

dimming ballast.

Table 5.5 Percentage energy savings over base case.

32%-35% West

30%-35% East

36%-43% South

28%-32% North

% Energy saving over base case Window Orientation

Fig. 5.15 Percentage average monthly electric lighting energy saving with proposed dimming control from 25/11/2010 to 25/02/2011 for window orientation in four cardinal directions.

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5.5 Chapter Summary

Energy saving can be attained from dimming/switching of artificial light as a function of

ambient daylight. The ambient daylight illuminance is often time dependent and nonlinear.

Designing a satisfactory control system necessitates precise online identification between

the real and identified model. In this chapter, adaptive predictive control using the ANFIS

model is proposed and the possibility of this control has been investigated through

experimental research. To evaluate the applicability the proposed control method and model

experiments in real time conditions are performed. The measurement datas used for online

modelling and control are obtained directly from external sunshine sensor and interior photo

sensors interfaced to PC. The proposed automated system maintains a base level of

illumination supplementing the interior daylight. Additionally, the controller is programmed

to shut lights off when spaces are unoccupied or exceeds the prescribed reference level. The

developed controller realizes a visual uniformity above 0.8. The analysis is provided during

office working hours for a period of four months from 8:00a.m to 5:00p.m (local time).

Nevertheless, the proposed control scheme implies that energy savings obtained is decisive

irrespective of the types of the luminaires, light sources and other electronic components

which may possibly reach above 30% which is very important in terms of energy savings. In

essence, online ANFIS dimming with fuzzy logic based window blind scheme optimizes

occupant comfort and energy savings. The developed adaptive predictive control schemes

progressively adapt themselves to the building and climate characteristics.

Documents

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