The membership functions for output variables: ceiling fan, air-water fan, water pump and heating unit are shown in the Table III.
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Autonomous Environment Control System using Fuzzy Logic
Abdul Salam Mubashar, M. Saleem Khan, Khalil Ahmad, Yousaf Saeed
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HERE are focused considerations for controlling en- vironment suitable to its living beings for not only easiness but also providing comfort to increase working capacities with fresh mind. It would save and enhance energy resources also. There have been criticalities for limitations of energy resources including ever increasing requirements for energy utilizations. There are wastages of energy during its utilizations even in needlessness spe- cifically in deregulated environments [1]. It can be over- come through proper scheduling but it involves hazards. An appropriate solution is to make autonomous and con- trolled systems according to the requirements. The pro- posed system is based on fuzzy logic. Fuzzy Logic is suit- able for uncertainties issues. The non-probabilistic prob- lems are dealt with fuzzy logic [3]. There are different fuzzy inference system and defuzzification techniques were reported [2], [6]. However this research provides comprehensive application of fuzzy Logic for the particu- lar appliances together involving the automation of light controller, air conditioner, heating unit ceiling fan and air-water-cooler controlling luminance, temperature and humidity. It would also save the energy cost. The feelings of temperature vary with the different levels of humidity. Humidity is the amount of water vapor in the air. In high humidity living beings feel the atmosphere very hot in summer because it reduces the effectiveness of sweating
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• Mr. Abdul Salam Mubashar is doing research in the field of Computer Science at National College of Business Administration and Economics (NCBA&E), Lahore, Pakistan (e-mail: salammunim@yahoo.com)
• Dr. M. Saleem Khan, Director Computer Science Department is with GC
University, Lahore, Pakistan (e-mail: mskgcu@yahoo.com)
• Dr. Khalil Ahmad, Director Computer Science Department National Col-
lege of Business Administration and Economics (NCBA& E), Lahore, Pa- kistan (e-mail: ahmedkhalil08@gmail.com).
• Mr.Yousaf Saeed is the faculty member & Ph.D. Scholar in the field of Computer Sciences at National College of Business Administration and Economics NCBA& E, Lahore, Pakistan (e-mail: usafon- line.email@gmail.com)
to cool the body by reducing the evaporation of perspira- tion from the skin. At normal temperature with wet at- mosphere, it would be felt cool by living beings because water vapors absorb energy rapidly [5]. Air-cooler ap- pliance works on this phenomenon. It includes air-cooler fan and water pump. It is something like window fan [7]. The phenomenon is also implemented for other ap- pliances to save energies. By decreasing or increasing humidity there is less energy required for controlling temperature and making appropriate luminance accord- ing to the specifications to minimize energy wastages and discomfort. The frame work of this paper comprises, de- sign and structure of the proposed autonomous environ- ment control system in section 2, section 3 gives the de- sign algorithm of autonomous environment control sys- tem, section 4 describes simulation results and discussion whereas Section 5 gives conclusion and future work.
There are included Fuzzyfier, Inference Kernel connected with Knowledgebase and Defuzzifier in the proposed fuzzy logic system. The Knowledgebase contains Data- base, Rule base and Membership Functions.
The fuzzifier converts the Crisp values into Linguis- tics values. The linguistics values are manipulated for inference engine [4]. The kernel of the system provides the output according to the Rule Selector. The Rule Selec- tor selects rules according to the Knowledgebase. Know- ledgebase comprises of Database, Rule base and Member- ship Functions [3]. Rule base is built up carefully consi- dering all possible effective situations. There have been incorporated four input variables: Luminance Intensity, Luminance Mode, Temperature and Humidity. There are six outputs controlling appliances: Luminance Controller, Air Conditioner, Ceiling Fan, Air-Cooler Fan, Water
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Pump and Heating Unit. There have been considered six- ty rules for the effective situations focusing energy sav- ing. There are used sixty operators for the rule selector. Luminance mode can be set according to the requirement. However there are proposed Time Oriented Luminance Intensity Mode Specifications for three types of premises including indoor and outdoor environments.
The feelings of hotness depend upon temperature and humidity. During low temperatures, the heating sys- tems work for long enough to convert the climate into dry resulting discomfort. At normal temperature heating sys- tem remains off while there wet climate effects cool.
There are six output variables to adjust and controle an environment for the five appliances. The membership functions for light control and air conditioner are represented in Table II.
TABLE II.
MEMBERSHIP FUNCTIONS OF OUTPUT VARIABLES: LUMINANCE
CONTROLLER AND AIR CONDITIONER
The membership functions for output variables: ceiling fan, air-water fan, water pump and heating unit are shown in the Table III.
TABLE III.
MEMBERSHIP FUNCTIONS OF OUTPUT VARIABLES: CEILING FAN, AIR-COOLER FAN, WATER PUMP AND HEATING UNIT
Fig.1. Block diagram of the proposed system to control the environ- ment in different premises using fuzzy logic.
Similarly at warmer temperatures dry humidity de- mands comfort. Such situations indicate deregulations that result in not only discomfort but energy wastages also. The proposed system deals with such scenarios. It can be applied for different types of premises including office, workplace, study, display, sleep, candlelight and street modes whether the premises contain or don’t con- tain all the appliances together. It would control lumin- ance, temperature and humidity.
Table IV shows the rule base for input luminance intensi- ty, luminance mode and corresponding output for lumin- ance controller.
The system is designed for five appliances. It takes four inputs namely luminance intensity, luminance mode, temperature and humidity. The membership functions with their respective ranges for input variables are shown in the Table I.
TABLE IV.
RULE BASE FOR LIGHT INTENSITY CONTROL
TABLE I.
MEMBERSHIP FUNCTIONS OF INPUT VARIABLES: LUMINANCE
INTENSITY, LUMINANCE MODE, TEMPERATURE AND HUMIDITY
Luminance intensity would be used to adjust the lumin-
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ance of the premises according to the requirement and the type of the premises. The requirments can be set with respect to the type of the premeses with time oriented scheduling in terms of luminance mode. A model sche- duling has been drawn for offic or work environmrnt and given in the Table V.
TABLE VIII
RULE BASE FOR TEMPERATURE AND HUMIDITY CONTROL IN THE PREMISES
TABLE V.
TIME ORIENTED LUMINANCE MODE SPECIFICATIONS FOR OF- FICE OR WORK PREMISES
For home premises a modulation for luminance control mode is represented in Table VI.
TABLE VI.
Time Oriented Luminance Intensity Mode Specifications for Home premises
Controling light in the outdoor environments is manupu- lated with the dierect daylight luminance. It can be im- plimented by both time oriented and the luminance sen- sor. However the lator is convinient. Table VII presents both ways.
TABLE VII.
TIME ORIENTED LUMINANCE INTENSITY MODE SPECIFICA- TIONS FOR OUTDOOR PREMISES
Table VIII presents rule base for inputs: temperature and humidity to controle output variables: ceiling fan, air- cooler fan its water pump, air conditioner and heating unit. Rules 17 to 24 are formulated for low temperatures when heating unit is critical while cooling appliances re- main off.
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Rules 25 to 33 cover the premises containing all the ap- pliances except heating unit. Rules 34 to 42 are for the premises that contain air conditioner and rules 43 to 51 reserve for just ceiling fan. Rules 52 to 60 cover the crtical- ities for air cooler including its water pump.
Plots of membership functions for input fuzzy va- riables are given in Fig. 2, Fig. 3, Fig. 4 and Fig. 5. The four membership functions Dark(1), Low(2), Normal(3) and Bright(4) are used to represent various ranges of in- put variable luminance intensity in Fig. 2.
Fig. 2. Plot of membership functions for input fuzzy variable Lumin- ance Intensity
The four membership functions Sleep / Off (1), Candle light/Safety (2), Normal daily Working (3) and Study/Display (4) are used to represent various ranges of input variable luminance intensity mode in Fig. 3.
Fig. 3. Plot of membership functions for input fuzzy variable Lumin- ance Mode
The seven membership functions Cold (1) Cool (2), Nor- mal(3), Warm(4), Very Warm (5), Hot (6) and Very Hot(7) are used to represent input variable Temperature in Fig. 4 containing six regions.
Fig. 4. Plot of membership functions for input fuzzy variable Temper- ature with their respective ranges
The four membership functions Wet (1) Nominal (2), Dry(3) and Very Dry(4) used to represent input variable Humidity in Fig. 5. It consists of three regions.
The four membership functions Off(1), Low(2), Me- dium(3) and High(4) luminance are used to represent fuzzy output variable Light Controller in Fig. 6. It con- tains three regions.
Fig. 6. Plot of membership functions for output Luminance Controller with their respective ranges
There are used six membership functions Very High (1) High(2), Medium(3), Low(4), Fan Mode (5) and Off (6) to represent output controlling variable for Air Conditioner in Fig. 7 containing five regions.
Fig. 7. Plot of membership functions for output variable Air condition- er with their respective ranges
There are used four membership functions High(1) Me- dium(2), Low(3) and Off(4) to represent output control- ling variable for Ceiling Fan in Fig. 8 consisting three re- gions. The third region is critical at high humidity.
Fig. 8. Plot of membership functions for fuzzy output variable Ceiling
Fan with their respective ranges
Although the rang values of Air-cooler Fan, Water Pump and Heating Unit are taken according to the requirment, however these are same. Therefore the shape of of the plot of membership functions for output variable Air- cooler Fan, its Water Pump and Heating Unit are occued same in this proposed design and are shown in Fig. 9.
Fig. 5. Plot of membership functions for Humidity with the ranges
Fig. 9. Plot of membership functions for output variable Air-Cooler
Fan, its Water Pump and Heating Unit with their respective ranges
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There are used three membership functions High (1) Low (2) and Off (3) to represent output controlling variable for Air Water Fan, its Water Pump and Heating Unit in Fig. 9 consisting of two regions.
The Fuzzy Fier will convert the three input crisp values into six output linguistic values. There are sixty rules are fired for the values of one set of variables. A to- tal of 60 rules have been generated. The inference engine consists of sixty operators. The rule selector receives four input values luminance intensity, luminance mode, tem- perature and humidity. It provides singleton values of output functions under algorithm rules applied on this design model. The Defuzzifier converts the output values into crisp values to control the environment.
Fig. 10. MATLAB rule viewer and simulations result for autonomous environment control system
The rule viewer represents output variations for different values of input variables according to the rule base.
The surface view of the plots of output variables lumin- ance controller, air conditioner, ceiling fan, air-cooler fan, water pump and heating unit on input variables lumin- ance intensity, luminance mode, temperature and humid- ity has been drawn according to the design scheme of the rule base. The plots in Fig. 11 indicate the inter dependen- cies of the effects of temperature and humidity on con- trolling units.
Fig. 11(a). shows that luminance intensity and lu- minance mode is directly proportional to the light con- troller. It means energy can be saved only through proper scheduling and does not by other than light resource.
Fig. 11(b). describe that ceiling fan directly propor-
tional to temperature in most of the ranges of the mem- bership functions while ceiling fan is not directly propor- tional to humidity in all the ranges of the membership functions. It shows that energy would be less consumed in the situations when ceiling fan is not depending on temperature and humidity.
Fig. 11(c). indicates the plot of air-cooler fan on tem- perature and humidity while energy would be saved in the situations when air-cooler fan is not depending on temperature and humidity.
Fig. 11(d). is the plot of water pump on temperature and humidity showing that it works smoothly in me- dium-low temperatures and high humidity while it is directly proportional to the humidity in high tempera- tures.
Fig. 11(e). indicates that energy is to be saved from heating unit having small values in high humidity with low temperature.
Fig. 11(f). depicts that energy is saving by reducing air conditioner in low temperatures.
Fig. 11(a). Plot view of luminance mode, luminance intensity on lu- minance controller.
Fig. 11(b) Plot view of ceiling fan on temperature and humidity.
Fig. 11(c) Plot view of air-cooler fan on temperature and humidity.
Fig. 11(d) Plot view of water pump on temperature and humidity.
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Mr. Abdul Salam Mubashar is doing Research in Artificial Intelligence at National College of Business Adminis- tration and Economics (NCBA&E) La- hore, Pakistan. His research area of interest includes Machine Cognition.
Fig. 11(e) Plot view of heating unit fan on temperature and humidity.
Fig. 11(f) Plot view of air conditioner on temperature and humidity.
The simulated results show the effectiveness of the Auto- nomous Environment Control System. It automates the appliances and contributes in better utilization of energy. The proposed system is suitable for deregulation envi- ronments also. It works from low humidity to high hu- midity, low temperature to high temperature and dark luminance to bright maintaining environment pleasant to the user satisfaction and comfort. It would enhance work- ing capacities also.
For future work neural network simulation of Auto-
nomous Environment Control System would be manipu- lated including more appliances.
Mr. M. Saleem Khan is an Assistant Professor at the GC University, La- hore, Pakistan. Currently he is work- ing as director Computer Science De- partment in GC University, Lahore, Pakistan. He availed research fellow- ship at The School of Electronics & Engineering, University of Edinburgh,
UK and completed his Ph.D. thesis in the field of control systems design, simulation and analysis in local and dis-
tributed environment. He contributed his services on var- ious projects in the field of Advanced Electronics and Communication. His research interests include control systems design and industrial applications. He promoted a large team of Electronics researchers and organized this field in his country. Mr. Khan had also been served as a senior scientific officer in a classified defense research organization in his country.
Mr. Khalil Ahmed is the Director School of Computer Science at Nation- al College of Business Administration and Economics (NCBA&E) Lahore, Pakistan. He is an expert academician and passionately engaged in research. His area of research is machine con- sciousness, A.I. and knowledge man-
agement.
[1] H. Salehfar, B. J. LaMeres et. al., “Fuzzy Logic Based Direct Load Con- trol of Residentional Electric Heaters and Air Conditioners Recognizing Customer Prefrences in a Deregulated Environment”, Proc. 1999 IEEE
[2] Shabiul Islam, Shakowat,”Development of a Fuzzy Logic Controller
Algorithm for Air-conditioning System”, ICSE 2006, Proc. 2006 IEEE.
[3] M. Saleem Khan et. al., “A Proposed Grinding and Mixing System using Fuzzy Time Control Discrete Event Model for Industrial Applica- tions”, Proc. 2009 IMECS
[4] B. P. Zeigler, P. Herbert, ”Theory of Modeling and Simulation, Integrat- ing Discrete Event and Continuous Complex Dynamic Systems” 1994, IEEE Press
[5] C.Michael Hogan., “Abiotic factor”, Encyclopedia of Earth. eds Emily Monosson and C. Cleveland. National Council for Science and the En- vironment. 2010, Washington DC
[6] Mircea Grigoriu, et.al. “Intelligent Buildings Energy Supply Following
Climate Parameters Variation Fuzzy Control” 2010, Proc. WSEAS.
[7] James Wiese, Maple Grove, “Window fan control system and method of controlling a fan unit”, 2009 US Patent application publication.
Mr. Yousaf Saeed is an Assistant Pro-
fessor at National College of Business
Administration and Economics (NCBA&E) Lahore. He has done his M.Phil. in Broadband Communication from University of Westminster, Lon- don, United Kingdom and now he is doing Ph.D. from National College of
Business Administration and Economics Lahore. His re- search area include Broadband and high Speed Commu- nication Networks.
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