HashemZeinaliBoroujeniMFKE2012TOC

vii TABLE OF CONTENTS
CHAPTER
1
TITLE
PAGE
DECLARATION
ii
DEDICATION
iii
ACKNOWLEDMENT
iv
ABSTRACTS
v
ABSTRAK
vi
TABLE OF CONTENTS
vii
LIST OF TABLES
x
LIST OF FIGURES
xi
LIST OF ABBREVIATIONS
xiv
LIST OF APPENDIX
xv
INTRODUCTION
1
1.1 Introduction
1
1.2 Problem Statement
2
1.3 Comfort conditions
3
1.4 Background of the Study
5
1.4.1 Intelligence buildings
5
1.4.2 Using controller toward energy saving
6
1.4.3 Fuzzy logic system as controller
6
1.5 Objectives of the Study
7
viii 2
3
1.6 Scopes of the Study
8
1.7 Thesis Organization
8
LITERATURE REVIEW
9
2.1 Literature Review Introduction
11
2.2 Classical controllers
11
2.3 Optimal, predictive, and adaptive control
12
2.4 Model-based optimal control for HVAC system
13
2.4
14
Lighting control systems for demand response
2.4.1 Daylight luminance
16
2.4.2 Integrated controller
17
RESEARCH METHODOLOGY
22
3.1 Mathematical model for building by using
fuzzy logic
22
3.1.1 Fuzzy logic system as controller
23
3.1.2 Fuzzy Logic Rules Use As Model
24
3.1.3 Main Operations
25
3.1.4 Rules-Based Systems
25
3.1.5 AI::Fuzzy Inference module
26
3.2 Definition of input and output variables
32
3.3 Inputs for fuzzy logic rules
34
3.3.1 Person activity and number of person
35
3.3.2 Out side light level
38
3.3.3 Times
42
3.3.4 Temperature and humidity
43
3.3.5 Natural ventilation and pressure
45
ix 3.4
4
5
Outputs data
47
3.4.1 lighting system
47
3.4.2 Air conditioner systems
49
3.4.3 Heating systems
52
3.4.4 Ventilation systems
53
3.4.5 Adjusting blinds
54
RESULTS AND DISCUSSION
55
4. 1 Introduction
56
4.2 General result from randomize inputs
57
4.3 Verification of result and discussion
63
CONCLUSION AND RECOMMENDATION
FOR FUTURE WORK
65
5.1 Conclusion
65
5.2 Recommendation for Future Work
67
REFERENCES
68
APPENDIX A
75
APPENDIX B
82
APPENDIX C
92
x LIST OF TABLES
TABLE NO.
TITLE
PAGE
3.1
Buildings model input/output and relevant symbol
32
3.2
the consumption of power in air conditioner system
50
xi LIST OF FIGURES
FIGURE NO.
TITLE
PAGE
1.1
Energy consumption rating in building,
Industry and transportation between 1980
to 2035 according to energy data book
2
Smart building using sensors and relevant
Actuator
5
Using fuzzy logic system as model for
Building power managing system including
sensors and actuators
7
2.1
Schematic of HVAC and control system
12
2.2
Overview of lighting system groups
14
2.3
Detailed scheme of the luminance control
loop
15
Principle block diagram of the three-nested
control loop levels
20
Basic block diagram of fuzzy logic
knowledge base system
23
3.2
Fuzzy set non-crowded
28
3.3
Fuzzy set ‘high’
30
3.4
Fuzzy inference system in Simulink
software
31
Comfort zone chart in the winter and
summer
34
3.7
No. of person fuzzy membership function
36
3.8
Person status fuzzy membership function
37
1.2
1.3
2.4
3.1
3.6
xii 3.9
Luminance efficiency for visible light
38
3.10
Spectral distribution of out side illuminance
39
3.11
The visible light wave length from outside
luminance
40
3.12
Member ship function for outside luminance
41
3.13
Fuzzy membership function of time
42
3.14
Fuzzy membership function of temperature
44
3.15
Fuzzy membership function of humidity
44
3.16
Pressure membership function
45
3.17
Lighting system fuzzy membership function
47
3.18
AC system fuzzy membership function
50
3.19
Heater system fuzzy membership function
51
3.20
Ventilation fan membership function
53
3.21
Blinds fuzzy membership function
54
4.1
Creating inputs connection in order to
apply to the fuzzy logic controller
57
Applying the randomize inputs to the
building model
58
Making the relevant rules connection
according to the inputs amount
59
The general output of the building
model system
59
The adjusting power consumption of
building’s lighting system based on
the randomize condition
60
The adjusting power consumption of
building’s air conditioner based on
the randomize condition
60
Adjusting power consumption of
building’s heater based on the randomize
condition
61
4.2
4.3
4.4
4.5
4.6
4.7
4.8
4.9
4.10
4.11
xiii Adjusting performance of building’s
ventilation fan system based on the
randomize condition
62
Adjusting angle of building’s windows
blinds system based on the randomize
condition
62
Applying the actual amount to the system
in order to verify result
63
Result of actual amount in order to
verification
64
xiv LIST OF ABBRIVATIONS
IAQ
-
Indoor Air Quality
PMV
-
Predictive Mean Vote
ASHRAE
-
American Society of Heating
Refrigerating and Air Conditioning
Engineers
CO2
-
Carbon Dioxide
TVOC
-
Total Volatile Organic Compounds
SBS
-
Sick Building Syndrome
DCV
-
Demand Controlled ventilation
AI
-
Artificial Intelligence
PID
-
Proportional–Integrate–Derivative
HVAC
-
Heating, Ventilating, and Air
Conditioning
RLS
-
Recursive Least Square
AHU
-
Air Handling Unit
FL
-
Fuzzy-Logic
LED
-
Light Emitting Diodes
FLA
-
Full Load Amp
LRA
-
Locked Rotor Amps
xv LIST OF APPENDIX
APPENDIX
TITLE
PAGE
A
Building fuzzy rule (person status -absent situation)
75
B
Building fuzzy rules (person status-present)
82
C
Building 3D fuzzy rule (person status –no of person)
92

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