report libre

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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.

Rizvi College of Engineering, Bandra, Mumbai. 1


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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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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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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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Figure 5.6: Time of the day Member functions

Figure 5.7: Compressor Speed Member functions



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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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