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A
Seminar Report On
AIR CONDITIONERS BASED ON FUZZY LOGIC
Submitted By
SHANKARESHWARI NADAR - 111P026SAYYED NAZNEEN NASIR - 111P031
DAKSHATA PADWAL - 101P001
Under the guidance of
Prof. MOHAMMED ASHFAQUE SHAIKH
Department of Computer Engineering
Rizvi College of EngineeringNew Rizvi Educational Complex, Off-Carter Road,
Bandra(w), Mumbai - 400050
Affiliated to
University of Mumbai
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Acknowledgements
I am profoundly grateful to Prof. Mohammed Ashfaque Shaikh for his expert guidance and contin-
uous encouragement throughout to see that this report rights its target since its commencement to its
completion.
I would like to express deepest appreciation towards Dr. Varsha Shah, Principal RCOE, Mumbai and
Prof. Dinesh B. Deore HOD Computer Department whose invaluable guidance supported me in com-
pleting this report.
At last I must express my sincere heartfelt gratitude to all the staff members of Computer Engineering
Department who helped me directly or indirectly during this course of work.
SHANKARESHWARI NADAR
SAYYED NAZNEEN NASIR
DAKSHATA PADWAL
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ABSTRACT
With the exponential increase in the use of cooling device, the air conditioning systems are becoming
an essential part of our day to day life. Data suggest an exponential rise in the use of air conditioners in
urban as well as rural India. With the increase in the usage of air conditioners, there is a simultaneous
increase in the electrical power consumption.According to the studies, by considering the input param-
eters we can greatly modify the functioning of the AC and reduce the electrical energy intake of the AC
compressor/Fan while utilizing all available resources in the efficient manner. The climatic condition
of Bhubaneshwar, India has been taken into consideration. Bhubaneshwar being in the coastal area, the
values of temperature and humidity are higher in comparison to non-coastal areas of India.
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INDEX
1 Introduction 1
1.1 Fuzzy set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2 Membership function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.3 Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
2 Working of an Air Conditioner 32.0.1 Disadvantages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
3 Fuzzy logic Air conditioner 5
4 Fuzzy logic controller 6
4.1 Data Base: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
4.2 Fuzzification: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
4.3 Fuzzy rule base: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
4.4 Fuzzy Inference machine: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
4.5 Defuzzification: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
5 Fuzzy variables 8
5.1 Fuzzy input variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
5.1.1 User temperature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
5.1.2 Temperature difference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
5.1.3 Dew point . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
5.1.4 Occupancy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
5.1.5 Time of the day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
5.2 Fuzzy Output Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
5.2.1 Compressor speed Member functions . . . . . . . . . . . . . . . . . . . . . . . 105.2.2 Mode of operation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
5.2.3 Fin direction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
6 Fuzzy Rule Base 14
7 Conclusion and Future Scope 16
7.1 Future Scope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
7.2 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
References 17
APPENDICES 17
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A Project Hosting 18
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List of Figures
2.1 Working of an air conditioner . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
4.1 Fuzzy logic controller/corruption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
5.1 User temperature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
5.2 Temperature diffrence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
5.3 Dew point table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
5.4 Dew point . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
5.5 Occupancy Member functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
5.6 Time of the day Member functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
5.7 Compressor Speed Member functions . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
5.8 Operation Member functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
5.9 Fin direction Member functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
6.1 description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
6.2 fuzzy base rule for dew point temperature at optimal value and occupancy at low and
time of dayis afternoon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
6.3 fuzzy base rule for dew point temperature at optimal value and occupancy at high and
time of dayis night . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
6.4 fuzzy base rule for dew point temperature at humid value and occupancy at high and
time of dayis afternoon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
6.5 fuzzy base rule for dew point temperature at humidl value and occupancy at medium
and time of dayis morning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
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Chapter 1 Introduction
Chapter 1
Introduction
Fuzzy logic is a form of many-valued logic it deals with reasoning that is approximate rather than fixed
and exact. Compared to traditional binary sets (where variables may take on true or false values) fuzzy
logic variables may have a truth value that ranges in degree between 0 and 1. Fuzzy logic has been
extended to handle the concept of partial truth, where the truth value may range between completely
true and completely false.WHERE DID FUZZY LOGIC COME FROM?
The concept of Fuzzy Logic (FL) was conceived by Lotfi Zadeh, a professor at the University of
California at Berkley, and presented not as a control methodology, but as a way of processing data by
allowing partial set membership rather than crisp set membership or non-membership. This approach to
set theory was not applied to control systems until the 70s due to insufficient small-computer capability
prior to that time. Professor Zadeh reasoned that people do not require precise, numerical information
input, and yet they are capable of highly adaptive control. If feedback controllers could be programmed
to accept noisy, imprecise input, they would be much more effective and perhaps easier to implement.
Unfortunately, U.S. manufacturers have not been so quick to embrace this technology while the Euro-
peans and Japanese have been aggressively building real products around it.
WHAT IS FUZZY LOGIC?
In this context, FL is a problem-solving control system methodology that lends itself to implemen-
tation in systems ranging from simple, small, embedded micro-controllers to large, networked, multi-
channel PC or workstation-based data acquisition and control systems. It can be implemented in hard-
ware, software, or a combination of both. FL provides a simple way to arrive at a definite conclusion
based upon vague, ambiguous, imprecise, noisy, or missing input information. FLs approach to control
problems mimics how a person would make decisions, only much faster.
HOW IS FL DIFFERENT FROM CONVENTIONAL CONTROL METHODS?
FL incorporates a simple, rule-based IF X AND Y THEN Z approach to a solving control problemrather than attempting to model a system mathematically. The FL model is empirically-based, relying
on an operators experience rather than their technical understanding of the system. For example, rather
than dealing with temperature control in terms such as SP =500F, T 1000F, or 210C TEMP
220C, terms like IF (process is too cool) AND (process is getting colder) THEN (add heat to the
process) or IF (process is too hot) AND (process is heating rapidly) THEN (cool the process quickly)
are used. These terms are imprecise and yet very descriptive of what must actually happen. Consider
what you do in the shower if the temperature is too cold: you will make the water comfortable very
quickly with little trouble. FL is capable of mimicking this type of behavior but at very high rate.
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Chapter 1 Introduction
1.1 Fuzzy set
A Fuzzy Set is any set that allows its members to have different grades of membership (membership
function) in the interval [0,1]. This definition of a fuzzy set is like a superset of the definition of a set in
the ordinary sense of the term. The grades of membership of 0 and 1 correspond to the two possibilities
of truth and false in an ordinary set.
1.2 Membership function
The Membership function of a fuzzy set is a generalization of the indicator function in classical sets.
In fuzzy logic, it represents the degree of truth as an extension of valuation. Degrees of truth are of-
ten confused with probabilities, although they are conceptually distinct, because fuzzy truth represents
membership in vaguely defined sets, not likelihood of some event or condition. Membership functions
were introduced by Zadeh in the first paper on fuzzy sets (1965).
1.3 Application
1.Fuzzy logic in washing machine
2.Fuzzy logic in Robotics
3.Fuzzy logic in Artificial Intelligence
4.Fuzzy logic in Data Mining
5.Fuzzy logic in Trafic Controll
6.Fuzzy logic in Other fields
Fuzzy logic can be applied in many other fields like business,hybrid modeling,expert system,finance,foreca
etc.
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Chapter 2 Working of an Air Conditioner
pressure. This hot gas goes through a series of condensing coils placed outside of the room being cooled.
The heat dissipates into the outside air, much like a cars radiator dissipates heat from the engine coolant.
Once the refrigerant reaches the end of these coils, it is significantly cooler and in liquid form.
This liquid is still under high pressure, like the contents of an aerosol can. In the case of air con-
ditioning, the liquid refrigerant is forced through a very tiny opening called an expansion valve. The
liquid refrigerant comes out of the other end of the expansion a very small amount at a time. Because
the refrigerant evaporates at a much lower temperature than water, it begins to evaporate while travelingthrough another set of coils. It is this evaporation action that draws heat out of the surrounding air,
including the air contained in the room. The units fan blows across metal fins placed over these coils,
causing the sensation of cooling in the room.
At this point, the liquid refrigerant has become a cold gas again and re-enters the compressor, where
the entire process begins again until a thermostat registers a specific temperature and shuts off the com-
pressor. When the room warms up, the thermostat senses the added heat and the compressor kicks back
on to create more of the hot pressurized gas. At some point, the temperature of the room may equal
the cooling power of the air conditioner and the compressor will shut off again. The air conditioning
systems of most houses do benefit from energy-saving steps such as using window shades and keepingdoors closed, since they dont have to work as hard to keep the room at an acceptable level of cool.
2.0.1 Disadvantages
1. It consumes more energy.
2. It is not very efficient.
3. It is difficult to implement complex problems.
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Chapter 3 Fuzzy logic Air conditioner
Chapter 3
Fuzzy logic Air conditioner
The task of dehumidification and temperature decrease goes hand in hand in case of conventional AC.
Once target temperature is reached AC seizes to function like a dehumidifier. Also complex interactions
between user preferences, actual room temperature and humidity level are very difficult to model math-
ematically. But in this study this limitation has been taken into cogitation and overcome to a great extent
using fuzzy logic to represent the intricate influences of all these parameters. The optimal limits ofcomfort zone, typically marked at a temperature of 25C and dew point 11C, are used as the targets. Con-
ventional AC system controls humidity in its own way without giving the users any scope for changing
the set point for the targeted humidity unlike the scope it offers to change the set point for the targeted
temperature through a thermostat. This causes a significant level of flexibility as well as efficiency loss
especially in hot and humid countries like India. For instance at higher humidity level (say at dew point
18C) an occupant may perceive same comfort level at 22C as he would perceive at 26C at dew point
15C. This translates to huge energy and monitory saving in terms of reduced compressor/fan duty cycle.
In the developed scheme, the sensor captured temperature, user temperature preference and humidity
readings are fuzzified. These are used to decide the fuzzy qualifier, which is decoded into a crisp value
that in turn controls different aspects of the AC. In the problem dew point (Td) temperature is used to
measure humidity instead of relative humidity (RH), this is because RH is a function of both temperature
and moisture content while Td is a function of moisture content only. Hence it becomes very easy to
model comfort level on the basis of Td. Human reaction to different levels of dew point.
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Chapter 4 Fuzzy logic controller
Chapter 4
Fuzzy logic controller
Figure 4.1: Fuzzy logic controller/corruption
Fuzzy Logic controller forms the base of the Fuzzy Control System. It basically consists of the
heuristics rules those define the parameters of the problem.It consist of
4.1 Data Base:
It normalizes the input crisp values and contains the fuzzy partitions of the input and output space.It
stores the valaues in it and helps in the further processing.
4.2 Fuzzification:
Fuzzification comprises the process of transforming crisp values into grades of membership for linguistic
terms of fuzzy sets. The membership function is used to associate a grade to each linguistic term.Fuzzification is the process of changing a real scalar value into a fuzzy value. This is achieved with
the different types of fuzzifiers. There are generally three types of fuzzifiers, which are used for the
fuzzification process; they are
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Chapter 4 Fuzzy logic controller
1.Singleton fuzzifier
2.Gaussian fuzzifier
3.Trapezoidal or triangular fuzzifier
4.3 Fuzzy rule base:
It contains the type of fuzzy rules and the source and derivation of the fuzzy control rules
4.4 Fuzzy Inference machine:
The basic function is to compute the overall output of the control output variable based on the
individual contribution of each rule in the Fuzzy Rule Base.
4.5 Defuzzification:
The basic function is to compute the overall output of the control output variable based on the
individual contribution of each rule in the Fuzzy Rule Base.
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Chapter 5 Fuzzy variables
Chapter 5
Fuzzy variables
The various variables for the Fuzzy Controller are:
5.1 Fuzzy input variables
5.1.1 User temperature
User Temperature (Ut) is the temperature provided by the user through remote controller or thermo-
stat. The range of this thermostat should vary between 18C and 30C. So the user set the temperature
accordingly.
Figure 5.1: User temperature
5.1.2 Temperature difference
Temperature Difference (Td) is measure of the difference in the actual room temperature and the tem-perature which is provided by the user .The difference range is between -6C to +6C. Also AC cannot
work as a heat pump and reverse its operation, so it is switched of once the difference go out of range.
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Chapter 5 Fuzzy variables
Figure 5.2: Temperature diffrence
5.1.3 Dew point
Dew point temperature is the temperature at which water vapor in the air will condense into dew, frost, or
water droplets given a constant air pressure. It can be defined alternately as the temperature at which the
saturation vapor pressure and actual vapor pressure are equal . Human reaction towards change in dew
point temperature can be generally established. Based on the data provided by Indian MeteorologicalDepartment, stationed at Bhubaneswar, a standard Dew Point Human Reaction table is generated. Based
on this table, the membership function for Dew Point is determined and given in Fig .
5.1.4 Occupancy
Occupancy is number of people exposed to air conditioner. The range of people will decide the level of
occupancy as low, medium or high. In the absence of people the compressor as well as the fan remains
off. We have taken into account the condition in a medium sized room. Level of Occupancy can also be
applied to shopping malls where if it lies between 1100 then its considered as low else between 101 to
300 as medium or else above 300 as high. The ranges can be varied according to various scenarios like
indoor stadiums, auditoriums, etc.
5.1.5 Time of the day
Time of Day is the period during which the AC would be working. The temperature and dew point
values vary significantly during morning or night time with that of afternoon time as per the data pro-
vided by IMD. Also the value of Relative Humidity changes nearly between 15 to 20 at 08.30hours and
17.30 hours. Accordingly the range of requirement can be decided for an optimum cooling and power
consumption. The range would be varied as 00.00 to 13.00 as morning, 09.00 to 18.00 as afternoon and
16.00 to 24.00 as night. User Temperature and Dew Point Temperature are ranged keeping in mind the
data provided by Indian Meteorological Department, Bhubaneswar. The ranges of these values can be
adjusted according to the specification of the area of operation of the AC.
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Chapter 5 Fuzzy variables
Figure 5.3: Dew point table
5.2 Fuzzy Output Variables
Following are the Output variables:
5.2.1 Compressor speed Member functions
The speed of compressor is varied between 30 to 100. Accordingly it will affect the room temperature
as per to the given input.
5.2.2 Mode of operation
Air conditioning system can act as a cooler as well as dehumidifier. In the cooling state it will regulate
the air to release cool air. But as dehumidifier it can absorb thehumid content of the air by passing dry
air into the room. This process does not increase the temperature of the room. This setting preference is
usually not given to the user and is performed implicitly by the AC. Considering this parameter leads to
greater efficiency and comfort levels.
5.2.3 Fin direction
The fins are the set of blades attached to the air conditioner to ensure a swift flow of air in a particular
direction. The direction of these fins will define the flow of air either towards or away from the user.
The angle of propagation of blades is set accordingly considering 0 degree as towards and 90 degree as
away.
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Chapter 5 Fuzzy variables
Figure 5.6: Time of the day Member functions
Figure 5.7: Compressor Speed Member functions
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Chapter 5 Fuzzy variables
Figure 5.8: Operation Member functions
Figure 5.9: Fin direction Member functions
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Chapter 6 Fuzzy Rule Base
Figure 6.3: fuzzy base rule for dew point temperature at optimal value and occupancy at high and time of dayis night
Figure 6.4: fuzzy base rule for dew point temperature at humid value and occupancy at high and time of dayis afternoon
Figure 6.5: fuzzy base rule for dew point temperature at humidl value and occupancy at medium and time of dayis morning
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Chapter 7 Conclusion and Future Scope
Chapter 7
Conclusion and Future Scope
7.1 Future Scope
In future we will come up with a device that implements the Fuzzy Logic controller in an embedded
system which can be used for increasing the efficiency of Air Conditioners.
7.2 Conclusion
Previously the Air-Conditioning systems which were used to simply cool the rooms now can perform
a variety of functions. By adding intelligence to the Air-Conditioning system we do not have to worry
about the cooling process. The analysis clearly maps out advantage of fuzzy logic in dealing with
problems that are difficult to study analytically yet are easy to solve intuitively in terms of linguistic
variables. In case of the Air-Conditioning system, fuzzy logic helped solve a complex problem without
getting involved in intricate relationships between physical variables. Intuitive knowledge about input
and output parameters was enough to design an optimally performing system. With most of the problemsencountered in day to day life falling in this category, like washing machines, vacuum cleaners, etc, fuzzy
logic is sure to make a great impact in human life.
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References
References
[1] Paper Name; Author Name, Conference Name, year etc
[2] Paper Name; Author Name, Conference Name, year etc
[3] Adaptive Steady State Genetic Algorithm for scheduling university exams,AlSharafat W.S.; Al-
Sharafat M.S., Networking and Information Technology (ICNIT), 2010 International Conference
on , vol., no., pp.70-74, 11-12 June 2010 doi: 10.1109/ICNIT.2010.5508555
[4] http://google.com
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Project Hosting
Appendix A
Project Hosting
The report is shared at Academia.edu. The complete report about the seminar is uploaded here for future
reference.
Report Link : http://www.academia.edu/attachments/6516122/download_file
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