International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org
Volume 4, Issue 3, March 2015
ISSN 2319 - 4847
Smart control of Air conditioning system for
thermal comfort
Zainab Siddqui1, Asif Jamil Ansari2, Ahmad Faiz Minai3
1
Department of Instrumentation & Control, Integral University, Lucknow, India
2
Department of Electrical & Electronics Engineering, Integral University, Lucknow, India
3
Department of Electrical & Electronics Engineering, Integral University, Lucknow, India
ABSTRACT
Rapid growth in building industry has increased the power demand.Smart buildings are becoming a trend to cope with the
energy needs and environment ease.This leads to the dependency of intelligent control. Reducing energy consumption and to
ensure thermal comfort are two important considerations in designing an air conditioning system. They contribute significant
part of total energy consumption. Studies suggest that in locations like auditoriums, indoor stadiums and conference halls, air
conditioning can contribute as much as 75% of total energy intake. The control strategy proposed is fuzzy logic controller
(FLC). The implementation of the Fuzzy Logic Control (FLC) system, allows arange of comfort to human body.A MATLAB
fuzzy toolbox is used where a fuzzy logic controller is for temperature, air quality and artificial lighting comfort parameters.
The presented control system is capable of achieving energy conservation in the buildings.
Keywords:-Fuzzy Control, Comfort Index, Energy Consumption, Smart Building.
1.INTRODUCTION
The productivity and quality of life of the people within a commercial building depends on comfort level within it. The
comfort factors also include visual and thermal comforts. These are provided by lighting and air conditioning systems.
More comfort is satisfied by more energy consumption. Due to energy scarcity these days, a balance between the energy
consumption and human comfort is required [1].
For reduction of power consumption and waste in buildings, an intelligent control system is needed, sinceenergy
consumption has been directly related to comfort and ultimately to operational costs. A building’s indoorenvironmental
primary comfort factors, according to the consumers’ preferences are thermal, visual and indoorair quality [2].
In this study a fuzzy logic control mechanism is developed for an indoor environmental control, to come across the
energy demand in the building, envelopes the users are central and dynamic entity and their preferences must be
accounted in the control system demand.The parametersranges are provided in the control system is in acceptable limits
of users.
2.BUILDING AUTOMATION AND FUZZY LOGIC CONTROLLER
An intelligent fuzzy control technique yield promising results and is applied to a substantial case in buildings (Dounis
et al., 2011; Alexandridis and Dounis, 2007; Lah et al., 2005). This indicating extensive total energy consumption
reduction in contrast to the existing control system, achieving the preferred comfort level. The peripheral fuzzy logic
controllers are employed to satisfy various comfort demands.
An indoor building environment is quite sensitive to variations and will closely follow the change. The nonlinear fuzzy
linguistic mapping model of an input data set to a scalar output data will overcome this. A FIScontains four basic
blocks: fuzzifier, rule sets, inference engine and defuzzifier. The general architecture isshown in Fig. 1. Initially, an
input set of crisp data is accumulated and turned into a fuzzy set utilizing fuzzylinguistic variables and membership
functions called fuzzification. An inference is developed based on the ruleset and finally, the fuzzy output is mapped to
crisp values using the membership function called defuzzification. [4].
Volume 4, Issue 3, March 2015
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International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org
Volume 4, Issue 3, March 2015
ISSN 2319 - 4847
Fig 1. Block Diagram of Fuzzy Logic Controller
3.FUZZY CONTROL FOR HVAC SYSTEM
The thermal has high impact on occupant’s productivity and satisfaction.Usually the thermal comfort index is
addressed as Predictive Mean Vote (PMV).
In this part the role of fuzzy modeling in heating, ventilation and air conditioning (HVAC) and control models are
presented.
The PMV index prevails within the range of -3 to +3. This has a variation occurrence in between -0.5 and +0.5, and
thus satisfies around 90% of the building dwellers [3]. The thermal comfort index has already been a prime feature in
PMV index computation and the building’s temperature has generally been specified with [5]. Both heating and
cooling techniques are associated with a one unit system actuator. The fuzzy control knowledge base inference consists
of two set strategies, one is comfort optimization and the second is energy consumption minimization. The input and
output membership functions are depicted in Fig. 2(a-c); whereas, the 3-D expert control plot is depicted in Fig. 3.The
knowledge base of the FIS system is shown in Table I.
Fig 2(a). Input 1 (Etemp)
Volume 4, Issue 3, March 2015
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International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org
Volume 4, Issue 3, March 2015
ISSN 2319 - 4847
Fig 2(b). Input 2 (EDtemp)
Fig 2 (c). Output (Ptemp)
Fig 3. Fuzzy Control Surface Plot for Temperature
Table 1. Knowledge Base for FIS Temperature Controller
NL NM NS Z
PS
PM PL
NL PL PL
PM PM PS
PS
NS
NM PL PL
PM PS
PS
NS NS
NS PM PS
PS PS
NS NS NM
Z
PM PM PS Z
NS NM NM
PS
PM PS
PS NS NS NM NM
PM PS PS
NS NS NM NL NL
PL
PS NS NS NM NM NL NL
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International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org
Volume 4, Issue 3, March 2015
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4.FUZZY CONTROLLER FOR LIGHTING
Daylight is a dynamic source of lighting and the variations in daylight can be quite largedepending on season, location
or latitude, and cloudiness. Different skylight levels can be found under the same sunlight conditions, and, even when
the sky pattern remains the same, the range of solar illuminances may increase as a result of a momentary turbidity
filter or scattering of particles over the sun. In consequence, any prediction system has to be flexible to allow for the
multivariate changes that characterize the combination of sunlight and skylight. The proposed day lighting fuzzy
control uses two sensing devices (an occupancy/motion sensor and a photo sensor), continuously electronic dimming
ballasts for every luminaries aiming the control of the electric lighting output, and a fuzzy controller.
A proposed algorithm is assigned to control the illumination:
if illuminance is between 500 and 550 lux and motion sensor is ON then all lamps is full powered else use the fuzzy
controller for lighting control
The input linguistic variables of the fuzzy controller are the level of the illuminancemeasured by the photo sensor while
the output variable is the level of the DC controlsignal. The fuzzy membership functions of Input/output variables are
shown in figures 4 and 5
Fig 4. Membership Functions of Input
Fig 5. Membership Functions of Output
Fig 6. Rule Base for the System
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International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org
Volume 4, Issue 3, March 2015
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Fig 7. Fuzzy Control Plot for Illumination
5.VENTILATION CONTROL
Air quality control space has been specified with level of CO2 concentration and is predominantly subjective tothe
concentration of pollutants in the indoor environment [7].
The indoor envelope air quality control space is predominantly subjective with the concentration of pollutants. It is
specified with the CO2 concentration (Emmerich and Persily, 2001) and represents the presence of the dwellers and
several pollution sources in the building (Zhu et al., 2010a). Thus CO2 concentration is used to indicate an air quality
index in the building envelope, measured in ppm. A fuzzy controller is applied to the slave air quality subsystem to
compute the power demand for the ventilator. The input of the local slave fuzzy controller is error among the outside
concentration and the internal set point. Thus the output is the required electrical power exploited for control of the
ventilation system.
Fig 8. Required power verses CO2 concentration
6.CONCLUSION
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 The study presented the comfort index of inhabitants
according to the power consumption pattern to make the wise decision of energy management though fuzzy logic
controller. MATLAB-simulation is used to achieve the designed goal. Three major systems were considered and a
fuzzy controller was developed for each of them .Thus Building energy management with intelligent control can
contribute the huge amount of energy savings and cost. This will make consumers aware properly to take wise actions
accordingly.
REFERENCES
[1] Pervez Hameed Shaikh, Nursyarizal Bin Mohd. Nor, Perumal Nallagownden, Irraivan Elamvazuthi, “Indoor
Building Fuzzy Control of Energy and Comfort Management”, Research Journal of Applied Sciences, Engineering
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International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org
Volume 4, Issue 3, March 2015
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and Technology 6(23): 4445-4450, 2013ISSN: 2040-7459; e-ISSN: 2040-7467, Maxwell Scientific Organization,
2013.
[2] Z. Wang, L. Wang, A.I. Dounis, & R. Yang, "Multi-agent control system with information fusion based comfort
model for smartbuildings," Applied Energy, vol. 99, pp. 247-254, 2012.
[3] A.I. Dounis, M. Santamouris, C.C. Lefas, “Building visual comfort control with fuzzy reasoning”, Energy
Conversion Management, vol - 34, pp. 17–28, 1993.
[4] J. Vernon,” Fuzzy Logic Systems”, control-systems-princiles.co.uk/pdf. accessed on dated: 03-05-2013.
[5] A. I. Dounis, C. Caraiscos, “Intelligent coordinator of fuzzy controller-agents for indoor environment control in
buildings using 3-D fuzzy comforter sets” In: IEEE International Fuzzy System Conference, London, UK; pp. 1–6,
2007.
[6] T.J. Ross, Fuzzy Logic with Engineering Applications, 2nd Ed, Hoboken, NJ: John Wiley & Sons, 2004.
[7] S. J. Emmerich and A. K. Persily, “State-of-the-art review of CO2 demand controlled ventilation technology and
application”, NationalInstitute of Standards and Technology, Technology Administration, US. Department of
Commerce. Pp. 1–43, 2001.
[8] ASHRAE Research, 2009. ASHRAE Handbook: Fundamentals. I-P Edn., American Society of Heating,
Refrigerating and Air-Conditioning Engineers Inc., Retrieved from: http://shop.iccsafe.org/media/
wysiwyg/material/8950P217-toc.pdf.
[9] P.O. Fanger, Thermal Comfort, “Analysis and Application in Environmental Engineering”, McGraw-Hill, New
York, 1972.
[10] Pervez Hameed Shaikh et.al / International Journal of Engineering and Technology (IJET) ISSN:0975-4024
pp:3236 – 3242 Vol 5 No 4 Aug-Sep 2013.
[11] Zhu, W., Y. Rui and W. Lingfeng, 2010b. Multi-agent control system with intelligent optimization for smart and
energy efficient buildings. Proceeding of the 36th Annual Conference on IEEE Industrial Electronics Society
(IECON). Glendale, AZ, pp: 1144-1149.
[12] Z. Peng, S. Suryanarayanan, X. Simo, and M. G. es., "An Energy Management System for Building Structures
Using a Multi-AgentDecision-Making Control Methodology," in Industry Applications Society Annual Meeting
(IAS), 2010 IEEE, pp. 1-8, 2010.
Authors
Zainab Siddiqui received her Bachelors of Technology in Electrical & Electronics Engineering from
Integral University, Lucknow, in the year 2010. During this period, was a part of various Instrumentation
and Control researches and societies. Currently pursuing Masters of Technology in Instrumentation and
Control from Integral University, Lucknow.
Asif Jamil Ansari is currently Asst. Professor in the Department of Electrical & Electronics Engineering at Integral
University, Lucknow. Pursuing Phd with the thesis titled, “Fuzzy Expert Systems”.
Ahmad Faiz Minai is currently Asst. Professor in Electrical & Electronics Engineering Department at Integral
University, Lucknow. He is Research Scholar in Development and Integration of Multi – Level Invertor for Solar PV
Application.
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