Upload
others
View
6
Download
0
Embed Size (px)
Citation preview
http://www.iaeme.com/IJCIET/index.asp 365 [email protected]
International Journal of Civil Engineering and Technology (IJCIET)
Volume 9, Issue 8, August 2018, pp. 365–377, Article ID: IJCIET_09_08_037
Available online at http://www.iaeme.com/ijciet/issues.asp?JType=IJCIET&VType=9&IType=8
ISSN Print: 0976-6308 and ISSN Online: 0976-6316
© IAEME Publication Scopus Indexed
OPTIMIZATION OF THERMAL COMFORT IN
OFFICE BUILDINGS USING
NON-TRADITIONAL OPTIMIZATION
TECHNIQUES
S. Elizabeth Amudhini Stephen
Associate Professor of Mathematics,
Karunya Institute of Technology and Sciences, Coimbatore, India
ABSTRACT
Due to the difficulty of controlling the indoor thermal environment, it is important
to provide thermal comfortable conditions which meet occupants’ expectation. In
order to realize the long-term thermal comfort in indoor environment, the
microclimate in Karunya university campus in Coimbatore, Tamilnadu. India is
measured through year. PMV model is applied to calibrate the climate parameters
and environment elements .The results obtained are optimized using ten different
nontraditional optimization models and compared to find which method is suitable in
terms of number trails and minimum time taken. ASHRAE standards are verified.
Key words: Indoor Thermal Comfort, Hot-Humid Regions, Non-traditional
Optimization Techniques.
Cite this Article: S. Elizabeth Amudhini Stephen, Optimization of Thermal Comfort
in Office Buildings Using Non-Traditional Optimization Techniques. International
Journal of Civil Engineering and Technology, 9(8), 2018, pp. 365-377.
http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=9&IType=8
1. INTRODUCTION
Thermal comfort is highly subjective, not only is it subject to personal preference but also to
varying temperatures. Both internal and external temperatures sensing is integrated in such a
way that the resulting effect would either move towards restoring deep body temperature or
move away from it.
A cold sensation will be pleasing when the body is overheated, but unpleasant when the
core is already cold. At the same time, the temperature of the skin is by no means uniform.
Besides variations caused by vasoregulation, there are variations in different parts of the body,
which reflect the differences in vasculation and subcutaneous fat. The wearing of clothes also
has a marked effect on the level and distribution of skin temperature.
Thermal comfort for human is one of the major problems at present. Providing thermal
comfort for occupants in buildings is really a challenging task because thermal comfort is not
S. Elizabeth Amudhini Stephen
http://www.iaeme.com/IJCIET/index.asp 366 [email protected]
only influenced by temperature but also factors like relative humidity, air velocity,
environment radiation, and activity level and cloths insulation. These entire six variables play
a major role in providing thermal comfort.
Thermal comfort can be calculated by an equation called Fanger‟s „Predicted Mean Vote‟
(PMV) as given by Fanger. This equation gives the optimal thermal comfort for any activity
level, clothing insulation and for all combinations of the environmental variables such as air
temperature, air humidity, mean radiant temperature and relative air velocity.
Human thermal comfort is defined by ASHRAE as the state of mind that expresses
satisfaction with the surrounding environment (ASHRAE Standard 55). Maintaining thermal
comfort for occupants of buildings or other enclosures is one of the important goals of design
engineers.
Thermal comfort is maintained when the heat generated by human metabolism is allowed
to dissipate, thus maintaining thermal equilibrium with the surroundings. Any heat gain or
loss beyond this generates a sensation of discomfort.
It has long been recognized that the sensation of feeling alone. The problem that we are
going to deal with here is the thermal hot or cold is not just dependent on air temperature
comfort of offices .
2. LITERATURE SURVEY
Human perception of air movement depends on environmental factors such as air velocity, air
velocity fluctuations, air temperature, and personal factors such as overall thermal sensation,
clothing insulation and physical activity level (metabolic rate) (Toftum, 2004). Air velocity
affects both convective and evaporative heat losses from the human body, and thus
determines thermal comfort conditions (Tanabe, 1988; Mallick, 1996). If we agree that
thermal environments that are slightly warmer than preferred or neutral, can be still
accepTable to building occupants as the adaptive comfort model suggests (deDear, Brager,
2002; Nicol, 2004), then the introduction of elevated air motion into such environments
should be universally regarded as desirable. This is because the effect will be to remove
sensible and latent heat from the body, so body temperatures will be restored to their comfort
set-points. This hypothesis can be deduced from the physiological principle of alliesthesia
(Cabanac, 1971).
In hot and humid climates, elevated indoor air velocity increases the indoor temperature
that building occupants find most comfortable. Nevertheless, the distribution of air velocities
measured during these field studies was skewed towards rather low values. Many previous
studies have attempted to define when and where air movement is either desirable or not
desirable (i.e. draft) (Mallick, 1996; Santamouris, 2004).
Thermal comfort research literature indicates that indoor air speed in hot climates should
be set between 0.2 - 1.50 m/s, yet 0.2 m/s has been deemed in ASHRAE Standard 55 to be the
threshold upper limit of draft perception allowed inside air-conditioned buildings, where
occupants have no direct control over their environment (de Dear, 2004) The new standard 55
is based on Fanger‟s (1970) draft risk formula, which has an even lower limit in practice than
0.2 m/s. None of the previous research has explicitly addressed air movement acceptability.
Instead it has focused mostly on overall thermal sensation and comfort (Toftum, 2002).
Optimization of Thermal Comfort in Office Buildings Using Non-Traditional Optimization Techniques
http://www.iaeme.com/IJCIET/index.asp 367 [email protected]
3. RESEARCH METHODS
3.1. Outdoor Climatic Environment
Under the Koppen climate classification, the Coimbatore city has a tropical wet and dry
climate. It has mild winters and moderate summers. Karunya University office buildings lie in
the latitude of 100 55‟ 51.73” N and longitude of 760 44‟ 40.60” E with elevation 1551 ft. The
surveys in this study were performed in the May 2009 and September 2009
3.2. Subjects
A Sample size of 220 subjects in 8 different office buildings in the Karunya University was
collected in survey and field measurements. The offices interviewed are multi-story buildings.
The volunteers participating in the study comprised both men and women. The average age of
all respondents was 33.2 years, ranging from 23 to 57 years. All the participants were in good
health. The questionnaire covered several areas including personal factors (name, gender, age,
etc.), years of living in their current cities and personnel environmental control.
The questionnaire also included the traditional scales of thermal sensation and thermal
preferences, current clothing garment and metabolic activity. The thermal sensation scale was
the ASHRAE seven point scale ranging from cold (-3) to hot (3) with neutral (0) in the
middle. The three point thermal preference scale asked whether the respondents would like to
change their present thermal environment. Possible responses were “want warmer”, “no
change”, or “want cooler”. Clothing garment check list were compiled from the extensive lists
published in ASHRAE -55, 2004. Metabolic rates were assessed by a check of activities
databases published in ASHRAE-55, 2004. The summary of the background characteristics of
the subjects are presented
Table 1 Summary of the Sample of Residents and Personal Thermal Variables
Sample Size 220
Mean 33.2
Age (year) Maximum 23 years
Minimum 5 months
Metabolic rate Clothing
insulation 75(W/m
2)
1.5 Clo
3.3. Data Collection
Both physical and subjective questionnaires were obtained simultaneously in the visit of the
field survey. This study investigates thermal environment and comfort of office buildings in
the Karunya University. A total of 220 subjects in naturally ventilated 11 office buildings (
with occupant – operable windows) provided 220 sets of cross-sectional thermal comfort data,
first field campaign from Mar 15, 2010 to Mar24,2010 and second field campaign from
Sep10,2010 to Sep 19, 2010 in Karunya University, Coimbatore. In both the set of data
collections the same buildings were taken into account for data collection. Indoor climatic
data were collected using instruments, with accuracies and response times in accordance the
recommendations of ANSI/ASHRAE 55. All the measurements were carried out between
10:00 hours and 16:00 hours.
A number of instruments were used to measure the thermal environment conditions, while
the respondents filled in the subjective thermal comfort questionnaire. The instruments were
standard thermometer for air temperature, whirling hygrometer for humidity, globe
thermometer for radiant heat, kata thermometer for air velocity. Metabolic rate can be
S. Elizabeth Amudhini Stephen
http://www.iaeme.com/IJCIET/index.asp 368 [email protected]
estimated using standard Table found in ISO 7730. Among the residential respondents, air
temperature readings were taken at a minimum of two locations in each space and at two
different levels corresponding to the body level and the ankle level corresponding to
approximately 0.1 m and 1.2 m above the floor level, respectively. Instruments used in this
study met the ASHRAE standards‟ requirements for accuracy.
During the survey period, there were no significant sources of radiant heat in residents‟
apartments. Therefore the operative temperature is close to the air temperature. The insulation
of clothing ensembles was determined using the Olsen‟s 1985 summation formula:
Icl= ∑ I clu,i where Icl is the insulation of the entire ensemble and I clu,i represents the
effective insulation of the garment i. The garments values published in the ANSI/ASHRAE
Stand card 55-2004 was the basis for the estimation of clothing ensemble insulation. The
general mean clothing-insulation value of 1.5 clo was recorded among all the respondents.
The majority of the respondents were seated on partly or fully upholstered chairs at the time
of survey. This appears to have been reflected in the generally higher mean value of 1.1 clo
recorded among the subjects at home.
The metabolic rates were determined from the activities filled by the subjects and as
observed at the time of the survey. Uniform value of 75 W/m2 was assumed for respondents
of the residential buildings. This assumption is based on the ISO 7730 Table of metabolic
rates for provisions for relaxed seating which was assumed to be the case with all subjects in
their homes.
3.4. Subjective Questionnaire
The subjective questionnaire consists of the following areas. All the surveys are “right now”
surveys. It takes 15 minutes in average for a participant to answer those questions.
Table 2 Range of values
Fcl Ta Tmrt Vair Pa Tcl M(met) Icl(clo)
Min 0 16 19.5 0.1 0.01 27 75 1.5
Max 1.5 34 23 1 1 29 75 1.5
Therefore the Problem is to minimize PMV for office
( ) *( ) , ( ) - ,( ) - ( ) ( )
,( ) ( )
- ( )+
( ) ,
,( ) ( )
- ( )-
{ ( )
( ) √
√ ( ) √
}
Subject to the following constraints (bounds)
0 ≤ Fcl ≤ 1.5;
Optimization of Thermal Comfort in Office Buildings Using Non-Traditional Optimization Techniques
http://www.iaeme.com/IJCIET/index.asp 369 [email protected]
16 ≤ Ta ≤ 34 ;
19.5 ≤ Tmrt ≤ 23;
0.1 ≤ Vair ≤ 1;
0.01 ≤ Pa ≤ 1;
27 ≤ Tcl ≤ 29;
M = 75;
Icl = 1.
4. ALGORITHMS
4.1. Genetic Algorithm
4.1.1. Options Set for the Algorithm
Initial population: 20.
Elite count: 2.
Cross over fraction as 0.8.
Max Time Limit: ∞.
Max Generations: 100.
Fitness Limit: -∞.
Selection: Stochastic.
4.1.2. Stopping Criteria:
If the maximum generations is reached (100).
If maximum time is reached (∞).
If average change in function value < 10¯⁶.
4.2. Simulated Annealing
4.2.1. Options Set
Initial Temperature: 100.
Annealing Function: Fast Annealing.
Reannealing interval: 100.
Time Limit: ∞.
Max.function evaluation: 3000 No. of variables.
Max. Iterations: ∞.
Function Tolerance: 10¯⁶.
Objective Limit: 10¯⁶
4.2.2. Stopping Criteria
Max. Time reached.
The average change in value of the objective function is < 10¯⁶.
S. Elizabeth Amudhini Stephen
http://www.iaeme.com/IJCIET/index.asp 370 [email protected]
Max. Iterations are reached.
If the number of function evaluations reached.
If the best objective function value is less than or equal to the value of Objective limit it is
stopped.
4.3. Pattern Search
4.3.1. Options Set
Poll Method: GPS positive Basis 2N.
Initial Mesh size: 1.
Expansion Factor: 2.
Contraction Factor: 0.5.
Mesh Tolerance: 10¯⁶.
Max. Iteration: 100 No. of Variables.
Max. Function Evaluation: 2000 No. of Variables.
Max. Time Limit: ∞.
Function Tolerance: 10¯⁶
4.3.2. Stopping Criteria
Mesh Tolerance: 10¯⁶.
Max. Iteration: 100 No. of Variables.
Max. Function Evaluation: 2000 No. of Variables.
Max. Time Limit: Inf.
Function Tolerance: 10¯⁶
4.4. Particle Swarm Optimization
4.4.1. Options Set
Max.Generation = 200.
Max. Time Limit= ∞.
Average change in fitness value= 10-6
.
Time Limit = ∞.
Function Tolerance= 10-6.
Cognitive Attraction = 0.5.
Population Size = 40.
Social Attraction = 1.25.
4.4.2. Stopping Criteria
Max.Generation = 200.
Max. Time Limit= ∞.
Average change in fitness value= 10-6
Time Limit = ∞.
Optimization of Thermal Comfort in Office Buildings Using Non-Traditional Optimization Techniques
http://www.iaeme.com/IJCIET/index.asp 371 [email protected]
Function Tolerance= 10-6
4.5. GODLIKE
4.5.1. Options Set & Stopping Criteria
Max.FunEvals = 10-5
.
Max. Iterations = 20.
Min. Iterations = 2.
Total. Iterations = 15.
Function Tolerance = 10-4
4.6. Fmincon
4.6.1. Options Set for ‘Fmincon’
Max.Iterations:400.
Max.function Evaluations: 100 No. of Variables.
Max.Time:∞.
Max. Function Tolerance: 10-6
.
4.6.2. Stopping Criteria for Global Search
Max.Time: Inf.
Max Wait cycle: 20
4.6.3.Stopping Criteria for Fmincon
Max.Iterations > 400.
Function Tolerance: 10-6
4.7. Direct Evolution
4.7.1. Options Set
Min. Value to Reach = 10-6
.
Population Size = 10 D.
Max. Iterations = 200.
Step Size F = 0.8.
Cross Over Probability = 0.5.
Strategy= 7 (DE/rand/1/bin)
DE/x/y/z, where DE stands for DE, x represents a string denoting the vector to be
perturbed, y is the number of difference vectors considered for perturbation of x, and z stands
for the type of crossover being used (exp: exponential; bin: binomial).
4.7.2. Stopping Criteria
Max.Value of function reached= 10-6
.
Max.Iterations=200
S. Elizabeth Amudhini Stephen
http://www.iaeme.com/IJCIET/index.asp 372 [email protected]
4.8. LGO
4.8.1. Stopping Criteria
If the current best solution did not improve for
Program execution time limits > 600 seconds.
4.8.2. Local Search Termination Criterion Parameter
first local search phase ends, if the function difference is less than
If max. constrain violation exceeds
4.9. glcCluster
4.9.1. Stopping Criteria
Maximum Iterations = 10000;
Maximum Function count = 10000;
Tolerance of Variables = 10-5
Function Tolerance =10-7
4.10. glcSolve
4.10.1. Stopping Criteria
Max.Iterations is exceeded > No. of variables 1000.
Max.function evaluations > No. of variables 2000.
If the difference of objective function is < 10-6
5. COMPARISON TABLE
Table 3 Comparative results of optimization methods for office thermal comfort.
PMV PPD -OFFICE
Methods Fcl Ta Tmrt Vair Pa Tcl PMV PPD TIME ITERS
GA 0.701 21.097 20.734 0.5073 0.2702 28.118 -0.5 5 0.53906 51
SA 0.710 20.647 21.367 0.5418 0.5946 28.231 -0.5 5 4.103220 3001
PS 0.75 25 19.545 1 0.255 28 -0.5 5 0.397420 26
PSO 0.80697 22.19925 21.58457 0.54836 0.47377 27.85813 -0.5 5 0.0954566 51
Godlike 0.84890 22.66393 20.96814 0.44038 0.555895 28.04283 -0.5 5 3.0026599 4
Fmincon 0.86431 23.18258 21.26676 0.555685 0.53481 28.22689 -0.5 5 14.68332 2288
DE optimization
SOLUTION
0.88907 23.46064 21.36051 0.664525 0.44529 28.20742 0.368 5 0.55702425 12000
LGO 1.2668 25.8503 20.8447 0.1 0.2887 28.5338 -0.5 5 1.0661718 3883
glcCluster 0.76 24.9946 21.4981 0.852 0.3922 28.6711 -0.5 5 0.6147302 1532
glcSolve 0.75 19 22.83333 0.25 0.0528 27.3333 -0.5 5 0.79695503 1771
Optimization of Thermal Comfort in Office Buildings Using Non-Traditional Optimization Techniques
http://www.iaeme.com/IJCIET/index.asp 373 [email protected]
-5
0
5
10
15
20
25
30
35
Fcl Ta Tmrt Vair Pa Tcl PMV PPD TIME
GENETIC ALGORITHM
SIMULATED ANNEALING
PATTERN SEARCH
PSO
GODLIKE
NON LINEAR
NUMERICAL optimizaion SOLUTION
LGO
glcCluster
glcSolve
Analytical
Figure 1 Comparative graph for office thermal comfort
The PMV and PPD have the same value as -0.5 and 5 for all the ten optimization
techniques except for DE, which has 0.36 as PMV. The elapsed time is maximum for fmincon
and minimum for PSO and PS. All the other parameter values are more or less the same for
all the ten optimization techniques. Now, the parameter values are taken separately and the
ten optimization techniques need to be compared so as to find which method is the best
method of optimization.
Table 4 Comparative Table for parameters in all 10 methods
Variable GA SA PS PSO GL Fmincon DE LGO Glc
Cluster
Glc
Solve Fcl X X 0.75
X X X X 1.26
0.76
0.75 Ta X X
25 X X X
25.8
24.9 19 Tmrt X X
19.54 X X X
20.84
21.49
22.83 Vair X X 1
X X X 0.1
0.852
0.25 Pa √
0.255
0.28
0.39
0.05 Tcl X X 28
X X X 28.533
28.67
27.33 PMV
-0.5
-0.5
-0.5
-0.5
-0.5
-0.5
-0.5
-0.5
-0.5
-0.5 PPD 5
5
5
5
5
5
5
5
5
5 Time 0.39 0.095
Iters X X 26 4 X X X X X
6. RESULT AND DISCUSSION
With the two extreme values of parameters from survey, the optimization is carried out with
different solvers. As they are of the stochastic type, their results may vary from trial to trial
and the problem is made to run for 20 trials (Elbeltagi, Tarek Hegazy, & & Grierson, 2005)
and an average of all trials is taken as the final value of the parameter, by the solver. The
solvers are compared with three different criteria.
6.1. Consistency
The consistency Table gives the parameters that remain constant for all the trials. All the
solvers give the same value of PMV& PPD for all the runs except DE, which in turn indicate
that the comfort requirements are in the acceptable range.
Fcl - P.S & glcSolve (0.75), glcCluster (0.76), LGO (1.26)
S. Elizabeth Amudhini Stephen
http://www.iaeme.com/IJCIET/index.asp 374 [email protected]
Ta - P.S (25), glcSolve (19), glcCluster (24.9), LGO (25.8)
Tmrt - P.S (19.54), glcSolve (22.83), glcCluster (21.49), LGO (20.84)
Vair - P.S (1), glcSolve (0.25), glcCluster (0.852), LGO (0.1)
Pa - P.S (0.255), glcSolve (0.05), glcCluster (0.39), LGO (0.28)
Tcl - P.S (28), glcSolve (27.33), glcCluster (28.67), LGO (28.53)
So we see that the solvers Pattern Search, glcSolve, glcCluster& LGO remain constant
throughout their runs.
Minimum Run Time
For a minimum run time of the problem, we got PSO (0.095 seconds), Pattern Search
(0.39 seconds).
Minimum Evaluation
This criterion will determine the effectiveness of the algorithm. From the result table, we
see that the Pattern Search and GODLIKE algorithms have minimum evaluation of 26 and 4
respectively.
Simplicity of Algorithm
Of all the algorithms we have taken, the Pattern Search algorithm is the most simplest
followed by GA, PSO, DE, Simulated Annealing, GODLIKE, Non-Linear, Direct algorithm.
Results according to Standards
This is the most important criterion that determines whether the solver is practical or not.
We got the standard values for a naturally ventilated building from ASHRAE as:
Humidity: 30% to 60%
(http://www.epa.gov/iaq/largebldgs/i-beam/text/hvac.html)
This gives that the Pa should lie within the range of:0.0765 to 0.501
Operative Temperature: 17.75 to 28.5
Air velocity:0.2 to 0.8 ms-1
(1 ms-1
only at extreme conditions)
With the above standards the solvers which adhere to the standard are:
Air-Velocity: Fmincon, GA, SA, PSO, GL, DE, glcSolve.
Partial vapour pressure: GA, PS, PSO, DE, LGO, glcCluster, glcSolve
Operative temperature: GA, SA,PS, PSO, Fmincon, DE, GL, LGO, glcCluster, glcSolve
The following Table gives a summary of all the criteria for the solvers:
Table 5 Summary of all the criteria for the solvers
Criteria GA SA PS PSO Fmincon DE GL LGO glcClus glcSolve
Result according to
ASHRAE
3/3
=100%
2/3
=67%
2/3
=67%
3/3
=100%
2/3
=67%
3/3
=100%
2/3
=67%
2/3
=67%
2/3
=67%
3/3
=100%
Consistency - - - - - -
Min-Run Time - - - - - - - -
Min-Evaluation - - - - - - - -
Simple Algorithm - - - - - - - - -
Optimization of Thermal Comfort in Office Buildings Using Non-Traditional Optimization Techniques
http://www.iaeme.com/IJCIET/index.asp 375 [email protected]
Thus it is seen that the Pattern Search solver satisfies all the criteria and scores 67% for its
practicality in giving result according to ASHRAE. So the appropriate algorithm, for
optimization of thermal comfort is suggested as Direct search algorithm & the solver is
PATTERN SEARCH
7. CONCLUSIONS
This study investigates thermal environment and comfort of office buildings in the Karunya
University. A total of 220 subjects in naturally ventilated 8 office buildings ( with occupant
– operable windows) provided 220 sets of cross-sectional thermal comfort data, first field
campaign from Mar 15, 2010 to Mar24,2010 and second field campaign from Sep10,2010 to
Sep 19, 2010 in Karunya University, Coimbatore. In both the set, the same buildings were
taken into account for data collection. Indoor climatic data were collected, using instruments
with accuracies with the recommendations of ANSI/ASHRAE 55. All the measurements were
carried out between 10:00 hours and 16:00 hours.
In the experiment conducted using ten non-traditional optimization techniques, the
thermal sensation takes the value -0.5, which is in the acceptable range , where the acceptable
range is -0.5 to +0.5 (ANSI/ASHRAE55-2004, 2004). From the thermal comfort value, we
can conclude that the thermal comfort of the office buildings of the Karunya University is in
the acceptable range and hence the thermal comfort in this area is optimum.
Here, ten non-traditional optimization algorithms were presented. These include: GA, SA,
PS, PSO, GL, FMINCON, EA, LGO, glcCluster, glcSolve. A brief description of each
method is presented along with a Pseudo code to facilitate their implementation. MATLAB
programs were written to implement each algorithm. The thermal comfort problem for the
offices of the Karunya University was solved using all algorithms, and the comparative results
were presented.
REFERENCES
[1] ANSI/ASHRAE55-2004. (2004). Thermal Environmental conditions for Human
occupancy. Atlanda, USA: American Society of Heating, Refrigerating and Air-
Conditioning Engineers.
[2] ASHRAE. (1989). Handbook-fundamentals, chapter 8,, Physiological Principles, Comfort
and Health.
[3] ASHRAE, A. s.-2. (2007). Design for accepTable indoor air quality,. Atlanta: American
Society of Heating, Refrigerating and AIr-conditioning Engineers, Inc.,.
[4] Auliciems.A. (1984). Thermobile controls for human comfort. Heating and ventlating
Engineeers , April/May 31-33.
[5] Cabanac.M. (1971). Physiological role of pleasure. Science , V17, 1103 - 1107.
[6] Coome, D., Gan,G, & Awbi,H.B. (1992). Evaluation of Thermal comfort and indoor air
quality. Proc.CIB'92 World Building Congress, (pp. 404-406). Montre'al.
[7] Culp, C., Rhodes, M., Krafthefer, B., & Listvan, M. (1993). Silicon Infrared Sensors for
Thermal comfort and Control. ASHRAE Journal , April 38-42.
[8] de Dear, R. a. (2002). Thermal comfort in naturally ventilated buildings:revisions to
ASHRAE Standard55. Energy and Buildings , 34 (6),pp 549-561.
[9] de Dear, R. (2004). Thermal comfort in practise. Indoor Air , vol 14, 32-39.
S. Elizabeth Amudhini Stephen
http://www.iaeme.com/IJCIET/index.asp 376 [email protected]
[10] Elbeltagi, E., Tarek Hegazy, 1., & & Grierson, D. (2005). . Comparison among five
evolutionary-based optimization algorithms. . Advanced Engineering Informatics , 19, 43-
53.
[11] Fanger, P., & Toftum, j. (2002). Extension of the PMV model to non-air-conditioned
buildings in warm climates. Energy and Buildings , 34,PP 533-536.
[12] Fanger.P.O. (1970). Thermsl comfort: Analysis and Applications in Environmental
Engineering. New York: McGraw-Hill
[13] Fanger.P.O., T. (2002). Extension of the PMV model to non-air-conditioned buildings in
warm climates. Energy and Buildings , 34(6), 533-536.
[14] Farzaneh, Y., & Tootoonchi, A. R. (2008). Controlling automobile thermal comfort using
optimizes fuzzy controller. Applied Thermal Engineering , Vol9 N04 1367-1376.
[15] Federspiel, C. (1882). Used-adapTable and minimum-power thermal comfort Control.
Ph.D Thesiis,MIT,Department of Mechanicla Engineering.
[16] Fountain, m., Brager, G., Arens, E., Bauman, F., & Benton, C. (1994). Comfort Control
for short-term occupancy. Energy and Buildings , 211-213.
[17] Freire, Z., Roberto, H.C.Oliveira, G., & Nathan.Mendes. (2008). Predictive controllers for
thermal comfort optimization and energy savings. Energy and Buildings , 1353-1365.
[18] Gan, G., & Croome, D. (1994). Thermal comfort models based on Field measurements.
ASHRAE Transactions , 782-704.
[19] Hamdi, M., Lachiver, G., & Michaud, F. (1999). A new predictive thermal sensation index
of human response. Energy and Buildings , 167-178.
[20] Hancock, P., Ross, J., & Szalma, J. (2007). A meta-analysis of performance response
under thermal stressors. Human factors , 49 (5),851.
[21] Humphreys, M., & Nicol, J. (2002). The validity of ISO-PMV for predicting comfort
votes in every thermal environments. Energy and Buildings , 34, 667 - 684.
[22] InternationalStandardISO7730. (1994). Moderate thermal environments- Determinatiob of
the PMV and PPD indices and specification of the conditions for thermal comfort.
[23] Int-Hout, D. (1990). Thermal Comfort Calculations/A computer model. ASHRAE
Transactions , 96(1) 840-844.
[24] Khodakarami, J. (2009). Achieving thermal comfort. VDM Verlag , ISBN 978-3-639-
18292-7.
[25] Leon, L. (2008). Thermoregulatory responses to environmental toxicants: the interaction
of thermal stress and toxicant exposure. Toxicology and Applied pharmacology , 233 (1),
146.
[26] MacArthur, J. (1986). Humidity and predicted-mean-vote-based PMV-Based Comfort
control. ASHRAE Transactions , 15-17.
[27] Mallick, F. (1996). Thermal COMFORT AND Building DESIGN IN THE TROPICAL
CLIMATES. Energy AND Buildings , 23, 161 - 167.
[28] MinNing, & Zaheeruddin, M. (2010). Neuro-optimal operation of a variable air volume
HVAC&R system. Applied Thermal Engineering , 385-399.
[29] Nicol, F. (2004). Adaptive thermal comfort standards in the hot humid tropics. Energy and
Buildings , 36, 628 -637
[30] Nicol, J., & Humphreys.A'. (2001). Adaptive thermal comfort and sustainable thermal
standards for buildings. conference Moving thermal comfort Standards into the 21st
century, Windor,UK , 5 -8.
Optimization of Thermal Comfort in Office Buildings Using Non-Traditional Optimization Techniques
http://www.iaeme.com/IJCIET/index.asp 377 [email protected]
[31] Nicol, J., Raja, I., ALLudin, A., & Jamy, G. (1999). Climatic variation in comforTable
temperatures: the Pakistan Projects. Energy and Buildings , 30, 261 - 279.
[32] Ning, M., & Zaheeruddin, M. (2010). Neuro-optimal operation of a variable air volume
HVAC&R system. Applied Thermal Engineering , 385-399.
[33] Oseland.N.A. (1995). Predicted and reported thermal sensation in climate chambers,
offices and homes. Energy and Buildings , 23(2) 105-115.
[34] Santamouris, M. (2004). Adaptive Thermal Comfort And Ventilation. Air Infiltration And
Ventilation , 18-24.
[35] Sherman, M. (1985). A Simplified Model of Thermal Comfort. Energy and Buildings , 8,
37-50.
[36] Smolander, J. (2002). Effect of cold exposure on older hmans. International Journal of
Sports Medicine , 23(2), 86.
[37] Soyguder, S., & Ali, H. (2009). An expert system for the humidity and temperature
control in HVAC systems using ANFIS and optimization wiht fuzzy modelling approach.
Energy and buildings , 41, 814 - 822.
[38] Stoops, J. (2004). A possible connection between thermal comfort and health. Retrieved
october 2010, from Lawrence Berkeley National Laboratory,Paper 55134:
http://repositories.cdlib.org/lbnl/LBNL-55134/
[39] Tanabe, S. (1988). Thermal comfort requirements in Japan. Waseda Univeristy: Doctoral
Thesis.
[40] Thompson, R., & Dexter, A. (2009). Thermal Comfort Control based on Fuzzy Decision-
making.
[41] Toftem, J. (2002). Human response to combined indoor environment exposures. Energy
and Buildings , 34(6),601-606.
[42] Toftum, J. (2004). Air movement - Good or bad? Indoor Air , 14,pp 40 -45.
[43] Toftum, J. (2005). Thermal comfort Indices. Boca Raton: Handbook of Human Factors
and Ergonomics Methods,63,CRC Press.
[44] Wang, L., & Mendel, J. (1992). Generating fuzzy rules by learning from examples, . IEEE
Trans. on Systems, Man, Cybernetics , 22 (6) 1414–1427.
[45] Xia, Y., R.Y, & Zhao, Y. J. (1999). Thermal comfort in naturally ventilated houses in
Bejjing. HV&AC , 1-5.
[46] YadollahFarzanth, & Tootoonchi, A. R. (2008). Controlling automobile thermla comfort
using optimizaed fuzzy controller. Applied Thermal Engineering , vol9,no.4,pg 1367-
1376.
[47] (n.d.). Retrieved from http://www.epa.gov/iaq/largebldgs/i-beam/text/hvac.html.
[48] (n.d.). Retrieved from http://www.engineeringtoolbox.com/water-vapor-saturation-
pressure-air-d_689.html.