JOURNAL OF APPLIED SCIENCES RESEARCH

Copyright © 2015, American-Eurasian Network for Scientific Information publisher
JOURNAL OF APPLIED SCIENCES RESEARCH
ISSN: 1819-544X
EISSN: 1816-157X
JOURNAL home page: http://www.aensiweb.com/JASR
2015 May; 11(8): pages 13-28.
Published Online 10 May 2015.
Research Article
The Residential House Thermal Comfort Comparison in Tropical Coast Area and
Mountain Area, by Adopting Static and Adaptive Approach
1Hermawan, 2Eddy
Prianto, 2Erni Setyowati
1DepartmentofArchitecture,Quranic
2Tropical
ScienceUniversity,Wonosobo,56351, Indonesia.
Architecture Building Technology Laboratory (TBA), Department of Architecture, Diponegoro University, Semarang, 50275, Indonesia.
Received: 25 March 2015; Revised: 14 April 2015; Accepted: 5 May 2015
© 2015
AENSI PUBLISHER All rights reserved
ABSTRACT
Static thermal comfort is a model which applies the theory of Predicted Mean Vote (PMV), invented by Fanger, whereas adaptive thermal
is a model based on test results of field studies that is commonly known as Actual Mean Vote (AMV). Residence on the coast and
mountain has different thermal variables (extreme). In tropical area, the coast possesses the hottest temperature, while themountain owns
the coldest. The difference on thermal condition will cause the different thermal comfort. Therefore, it is necessary to see the different
thermal comfort from the statistic of thermal comfort and the adaptive thermal comfort. The purpose of this study is to analyze the
application of PMV and AMV in residential housesat tropical coast and mountain, and to discover the difference between them. In
addition, this study observed the other thermal comfort calculations such as To, ET*, SET, PPD, Tn(humphreys) and Tn(auliciems). This is a
quantitative research which conducted field surveys by using both thermal tool and questionnaire to collect subjective data. This study
discovered that the results showed the average of PMV for tropical coast residential house that was +0.85 (neutral, incline to rather warm),
whereas for the mountain residential house the average of PMV was +0.50 (mid, between neutral toward rather warm). The AMV, on the
other hand, showed the result ofresidential house attropical coast was -0.70 (neutral, incline to rather cool), while the residential house at
mountain area was -0.52 (mid, between neutral and rather cool).
Keywords: static approach; adaptive approach; tropical coast, tropical mountain
INTRODUCTION
Adaptive thermal comfort has been developed
and been used by many researchers. The adaptive
thermal comfort starts when Humphreys and Nicole
find out that static thermal comfort model is not
suitable for naturally ventilated buildings especially
in warm and humid climate. However, as many
studies have been conducted, it is also discovered
that there are some findingssuggesting that static
thermal comfort model is not suitable for artificially
ventilated building (with AC) in warm and humid
area,revealed by the study conducted in Malaysia.
There are some researches of building with
natural ventilation, one of them is a research of
housing in Cameroon conducting a field study for
three areas i.e.the coast, low land and mountain. This
research is conducted by seeing the value of PMV on
each area before doingthe field study to find AMV
value. The field study uses thermal sensation 7 points
scale from ASHRAE (American Standard Heating
Refrigerating, Air Conditioning, Engineers). The
result gained is that the comfort range of the
occupantsisinfluenced by the location of the housing.
PMV cannot predict the occupants comfort well so it
is necessary an improvement of the PMV model [1].
This matter is in line with Humphreys and Nicol’s
research considering that PMV cannot predict the
good thermal comfort of the building with natural
ventilation.
A research on nine hospital buildings is
conducted in Malaysia, which involves 293 workers.
Corresponding Author: Hermawan, Department of Architecture, Quranic Science University, Wonosobo, Indonesia.
Tel:+62-286321873, Fax:+62-286324160, E-mail:[email protected]
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Hermawan et al, 2015 /Journal Of Applied Sciences Research 11(8), May, Pages: 13-28
This research analyzes the operative temperature and
behavior adaptation. The acceptably comfortable
temperature is 23.3oC-26.5oC, with outdoor
temperature between 25.4oC and 35.0oC. Most
occupants feel comfortable at 26.4oC. The study
brings practical result for planners to have energy
saving and to minimize the use of Air Conditioner
for high rise building [2].
Besides the field study, some researchers also
discuss about the comfort adaptive thermal from
theory point of view. The result of this research
enables the researcher to give recommendation for
energy saving. On the other hand, the result from the
theoretical studies is that, as yet, there is no adaptive
thermal comfort theory which is accepted globally
since desired expectation of occupants about thermal
comfort is different [3].
Adaptive thermal comfort constitutes thermal
comfort based on the occupants’ comfort; therefore,
the data are collected with questionnaire. The scale is
using 7-point scale from ASHRAE, whichis used by
some researchers [4]. Notwithstanding, PMV is a
basis of thermal comfort analysis like a balcony
influence towards thermal comfort. This occurs in
passive thermal comfort that is supposed to see the
element effectiveness in building. In this research, a
balcony has an effect to raise the airflow into the
building. A simulation research by using CFD,
adopting Fanger theory (PMV-PPD), is also
conducted to observe the effect of the balcony in
creating thermal comfort in a naturally ventilated
building. The result obtained is the PMVnv numbers
which improve the performance of PMV. This
research enables planners to consider the use of
balcony in designing upper floor by having the
knowledge about the effect of it towards thermal
comfort [5].
Some researchers also clarify the variables that
influence the thermal comfort by conducting
researches such as the study of solar ray radiation
temperature average. The study about the solar ray
radiation temperature average is conducted to
observe the influence of windows coating towards
Tmrt and thermal comfort in a room. The result of the
study shows that small and long window is
considered better than the square one in reaching the
thermal comfort [6].
Another study is conducted regarding airflow in
thermal. The purpose of this study is to observe the
airflow effect towards thermal comfort in a test room
with natural ventilation. This study is conducted at 4
different locations and at 6 different levels. The
predicted thermal comfort indicates that the value of
PPD in summer season is significantly increasing
with higher temperature between indoor and outdoor
for high speed wind [7].
The other research combining the statistic and
adaptive thermal comfort is also conducted with
experimental method. This research analyzes the
neutral temperature and is related to operative
temperature and external temperature by using linier
regression analysis. The result is
PMV = 0.3870To – 8.8784
0 = 0,3879Tn – 8.8784
Tn – To – PMV/0.3879
Tn = 0.7677To + 4.5842
Tn = 0.1544Te + 19.3520
With PMV = Predicted Mean Vote, Tn =
Temperature Neutral, To = Temperature Operative,
Te = Temperature External [8]. This is in line with
other researches that consider that PMV needs to be
enhanced with the field study, so there are inventions
of PMV model that are enhanced. [9]
Some researchers suggest reviewing the concept
of PMV towards some buildings at different
locations before applying the adaptive theory. Should
there be any incompatibility found, it is necessary to
use the concept of AMV as the basis of study
regarding thermal comfort towards the building, like
the similar case at office buildings in Malaysia. A
study in regards to thermal comfort level with some
layouts of open windows and doors measures the
value of PMV and PPD. It also measures the
concentration of CO2. Collection of subjective data is
also conducted. The data show that the comfort level
is far below the standard of ASHRAE 55. The
subjective thermal comfort is higher than PMV. This
has become a rational consequence as the
international standard is not suitable for buildings in
a humid and warm climate area. The concept of
PMV comes from Europe, in which the occupant’s
preference is different. The occupants manage a
certain window control to get accustomed with their
surrounding climate [10].
The research of thermal comfort with residence
as the object also occurs in Thailand by comparing
two elements that are indoor rooms and outer sheath.
This method used is multi faced methodology with a
program or software modeling. The result is a
variable void and interior producing different
temperature air flow in refrigerator ranging 22–30o C
[11]
The study of thermal comfort on residential
house has been rarely published on international
journals. This is reasonably due to the constraints
found when field study is conducted. There are many
factors that have to be considered when conducting
the study. However, there are some studies on
residential houses. The study on traditional
Minahasan residential house, which used CFD
(Computational Fluid Dynamic), emphasizes on the
aspect of raising the floor. The raise of the floor is
studied and is connected with the movement of wind
in creating thermal comfort for the occupants. A
simulation is done on some opening variation. The
result of the study indicates that floor rising is still
considered effective in establishing thermal comfort
[12].
Another research also studies one of the thermal
comfort variables that is the speed of wind at the
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Hermawan et al, 2015 /Journal Of Applied Sciences Research 11(8), May, Pages: 13-28
balcony of a residential house. The research is
conducted by using software that is called N3S and it
uses simulation method. The result of the research
reveals that balcony helps the improvement of
natural airflow. In addition to balcony, there are also
some elements of the building which escalate the
wind movement [13,14].
Traditional building with vernacular architecture
has proved to be the one that provides thermal
comfort. Modern building, on the other hand, often
neglects traditional technique, construction material
and climate. This research conducts element analysis
in Nepal by applying qualitative approach.
The research of traditional and contemporary
residence in high land with field survey method has
been conducted in 1996 in Zambia by using
questionnaire. This research intends to clarify PMV
and finds out thermal comfort model in traditional
residence in high land. The invented model is CV =
0.99 + 0.11 Tg in summer and CV = 2.56 + 0.26 Tg
in winter. CV is Comfort Vote and Tg is
Temperature Globe [15, 16]. This model explains the
relationship between thermal comfort with
temperature globe, but has not yet explained about
the relationship of thermal comfort and other
variables such as operative temperature, air
temperature and so forth.
Thermal comfort research uses field survey
method and produces statistic model. It has been
done by taking data from other researches for
building with natural ventilation that produces Tcomf
model = 0.341Ta; out + 18.83 and doing correction
towards PMV and the result TSV = 0.82PMV –
0.358 with Tcomf is Temperature comfortable, Ta;
out is external air temperature, TSV is Thermal
Sensation Vote and PMV is Predicted Mean Vote.
This research explains that adaptive thermal comfort
depends on each local occupant, so it will produce
different research model.
A research about thermal comfort on vernacular
residence in India by comparing three different
climate areas and brings about the relationship
between the outfit and the operative temperature, Clo
– 0.038 TO + 1.454 (R2 – 0.817) for climate area1,
Clo = -0:046 – To + 1.65 (R2 = 0.774) for the
climate area 2, Clo = -0.056 – To + 1.93 (R2 =
0.869) for the climate area 3. Other result is the
correction towards PMV using questionnaire [18].
This research has not seen the relationship between
thermal Sensation Vote with outfit, operative
temperature or others. Comparing the three climate
areas needs to consider that adaptive thermal comfort
theory contains subjectivity of the occupants.
The difference of the climate area in Indonesia is
quite extreme in high landrepresented by mountain,
and low landrepresented by the coast. High land
possesses quite cold temperature while low land is
quite hot. Both are extremely different in tropical
area.
In line with other research [17, 18], it observes
that there is a difference in analysis result from
Predicted Mean Vote (PMV) and the result of field
study (Actual Mean Vote); therefore, it is necessary
to check out the thermal comfort by using basic static
thermal comfort (PMV) compared to the result of
field study (AMV).
Objectives:
The purpose of this research can be formulated
as follows:
a. To analyze the application of PMV on
residential house in tropical coast and mountain area.
b. To Conduct an AMV field test at residential
house in tropical coast and mountain area
c. To analyze the difference between PMV and
AMV, in order to obtain the deviation value of PMV
and AMV as a result of the big difference between
the occupants’ thermal comfort with both
analysis.This would be the reference that is necessary
to conduct an advanced research to make the comfort
as adaptive thermal comfort standard.
Method:
Indonesia is a tropical area that has two different
types in climate. That is the tropical coast with a high
temperature and mountainous area with a low
temperature. This kind of differences influences the
thermal sensation felt by the occupants. The thermal
sensation felt by the occupants becomes the success
indicator of residence type in realizing its thermal
comfort. The method used in this research was the
field study in two researched areas,the tropical coast
and the tropical mountain,under thermal comfort
study.
The determination of research area was based on
the geographical area. The tropical coast area was
located from the coastline up tomaximum altitude of
100 meter, and the tropical mountain area was an
area located near the mountain with minimum
altitude of 400 meter from the coastline. The tropical
coastal area was represented by Demak Regency,
Central Java, particularly in Morosari Village, while
the tropical mountain area was represented by
Wonosobo Regency, Central Java, particularly in
Buntu Village and Kapencar Village. Both areas
were located in one Province in order not to find the
big difference in culture or habit of the local
occupants.
The residential houses in tropical coast area that
were studied were the ones with wood walls and
exposed brick walls (non-plastered wall).
Sample taking is according to Singh’s research
in India. It observes the vernacular residence [19]
and based on the reality that they are coastline people
with all the consequence. Most residentswere middle
and lower class society who lived in vernacular
residence. It seemed that the most catching coastline
residence area was wooden wall residence and
exposedbrick. Regarding that condition, the coast
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Hermawan et al, 2015 /Journal Of Applied Sciences Research 11(8), May, Pages: 13-28
area residential house studied were the ones with
wood walls and exposed brick walls (figure 2).
The tropical mountain area has the opposite
temperature against the coast area. Common
residential houses in mountainous area are the ones
with river-stone wall and wood wall. Although some
houses had the walls plastered, there were more
houses with exposed river-stone wall (non plastered).
a
b
a
c
b
a
This could be categorized as vernacular residential
house, using natural resource as construction
material. Sample taking in tropical mountainous area
is also based on Singh’s research [19]. The samples
of residential houses, which were studied for
mountain area residential house, were the ones with
exposed river-stone and wood walls (figure 3).
d
c
b
a
e
Fig. 1: The research location (a) a map of Indonesia (b) a map of Central Java Province showing Demak
Regency (Coastal Area) and Wonosobo Regency (Mountain Area) (c) Morosari Village area (The
research area of wood and exposed brick walls Houses in Coastal Area) (d) Buntu Village area (The
research area of Wood Walls Houses in Mountain Area) (e) Kapencar Village area (The research area
of Stone Walls Houses in Mountain Area). Source: Google earth.
Fig. 2: Local residential house in tropical coast area; (a) exposed brick walls house and visual of respondents;
(b) wood walls house and visual of respondents.
Fig. 3: Local Residential House in Tropical Mountain Area;(a) exposed stone walls house and visual of
respondents;(b)wood walls house and visual of respondents.
The reason of residential house selection lied on
consideration to find materials that could be used to
build walls for buildings that were sustainable and
provides thermal comfort for the occupants.
Currently, construction materials, which indirectly
contribute global warming, have beenbeing
developed. A research to study building materials
from waste of Styrofoam products is also conducted
[20].
In passive thermal comfort, the building element
material completely determines the occupants’
thermal comfort. In line with that matter, samples in
this research were also taken from different wall
materialssuch as wood and the exposure of gravel
and brick.
The study was conducted by measuring thermal
variables (air temperature, globe temperature,
relative humidity, and wind speed). Subjective
measurement was also conducted by applying 7
17
Hermawan et al, 2015 /Journal Of Applied Sciences Research 11(8), May, Pages: 13-28
scales of thermal sensation from ASHRAE
(American Society of Heating, Refrigeration, Airconditioning Engineers) that are cold (-3), cool (-2),
neutral (0), rather warm (+1), warm (+2) and hot
(+3). The measurement was conducted in a day from
07:00 (Jakarta Time) until 17:00 (Jakarta Time).
While the measurement was in progress, subjective
data was also collected concerning subjective
thermal sensation for 3 times, in the morning, at noon
and in the afternoon [21].
It is necessary in statistics to collect minimal
data of 30 respondents. This research usedthe Vote
for Thermal Comfort. The vote was taken from
questionaire in one-day time. Each respondent was
asked to fill out the questionaire three times a day so
the minimal data required were 10respondents. Most
of the residenceswereoccupied by 2 occupants, while
othersweremore than two. On the other hand, there
weresome residences occupied by just one person.
Based on that fact, the residence was considered
occupied by two people so it needed at least five
houses in one climate area.
The sample taking was taken in 8 residences for
one area considering the requirement mentioned
above, 3 houses more (15% from the minimum
requirement).The samples used in this research
were8 residential houses for tropical coast area,
consisting of 4 exposed brick wall residential houses
and 4 wood wall residential houses. As for the
mountain area, 8 houses were also taken as samples,
consisting 4 river-stone wall residential houses and 4
wood wall residential houses. The surveys were
simultaneously conducted in the same day by
comparing the exposed brick wall residential houses
in coast area and the exposed river-stone wall
residential houses in the mountain area, and also by
comparing the wood wall residential houses in both
coast area and mountain area (Table 1).
Table 1: The comparison of surveyed housing sample.
Tropical Coast Area
Mountain Area
Exposed brick wall House
Exposed river stone wall house
Wooden wall house
Wooden wall house
Result and Discussion
There were 18 respondents in tropical coast area,
providing 54 set of data. Whereas in the tropical
mountainous area, there were 21 respondents,
Table 2: Respondents Profil in Coastal Area.
No
House Specification
Respondent Name
1
Exposed Brick Wall
jamila
2
Exposed Brick Wall
sabar ahmadi
3
Exposed Brick Wall
harto
4
Exposed Brick Wall
puji santoso
5
Exposed Brick Wall
temon
6
Exposed Brick Wall
nariyah
7
Exposed Brick Wall
ninda
8
Exposed Brick Wall
ropiah
9
Exposed Brick Wall
ludin
10
Wooden Wall
maskub
11
Wooden Wall
mafrifah
12
Wooden Wall
majyudi
13
Wooden Wall
abnah
14
Wooden Wall
hadi
15
Wooden Wall
yudho
16
Wooden Wall
resti
17
Wooden Wall
agus
18
Wooden Wall
marifah
Notes
The survey was conducted on the same day
The survey was conducted on the same day
providing 61 set of data. The different number of
respondents occurred as the numbers of occupants
were also different for each residential house. Profile
of respondent can be seen at Table 2 and 3.
Recapitulation data can be seenatfig 4 until fig 23.
Job
Labour
Labour
Retired
Student
Labour
Labour
Labour
Labour
Labour
Builder
Housewife
Builder
Housewife
Labour
Labour
Labour
Labour
Trader
Long Settled
10 years
10 years
80 years
19 years
10 years
10 years
10 years
10 years
10 years
38 years
12 years
65 years
55 years
10 years
10 years
10 years
10 years
26 years
Sex
Female
Male
Male
Male
Male
Female
Female
Female
Male
Male
Female
Male
Female
Male
Male
Female
Male
Female
18
Hermawan et al, 2015 /Journal Of Applied Sciences Research 11(8), May, Pages: 13-28
hadi
hadi
hadi
abnah
abnah
abnah
majyudi
majyudi
majyudi
mafrifah
mafrifah
maskub
mafrifah
maskub
maskub
sabar
sabar
sabar
jamila
jamila
jamila
200
180
160
140
120
100
80
60
40
20
0
7 12 5 7 12 5 7 12 5 7 12 5 7 12 5 7 12 5 7 12 5
am pm pm am pm pm am pm pm am pm pm am pm pm am pm pm am pm pm
age
height
weight
Du Bois (Adu)
Fig. 4: Graph of Coastal Area Respondent Profile(1).
temon
temon
temon
marifah
marifah
marifah
puji
puji
puji
harto
harto
agus
harto
agus
agus
resti
resti
resti
yudho
yudho
yudho
200
150
100
50
0
7 12 5 7 12 5 7 12 5 7 12 5 7 12 5 7 12 5 7 12 5
am pm pm am pm pm am pm pm am pm pm am pm pm am pm pm am pm pm
age
height
weight
Du Bois (Adu)
Fig. 5: Graph of Coastal Area Respondent Profile(2).
age
height
weight
ludin
ludin
ludin
ropiah
ropiah
ropiah
ninda
ninda
ninda
nariyah
nariyah
nariyah
200
150
100
50
0
Du Bois (Adu)
Fig. 6: Graph of Coastal Area Respondent Profile (3).
hadi
hadi
hadi
abnah
abnah
abnah
majyudi
majyudi
majyudi
mafrifah
mafrifah
mafrifah
maskub
maskub
maskub
sabar
sabar
sabar
jamila
jamila
jamila
120.00
100.00
80.00
60.00
40.00
20.00
0.00
7 12 5 7 12 5 7 12 5 7 12 5 7 12 5 7 12 5 7 12 5
am pm pm am pm pm am pm pm am pm pm am pm pm am pm pm am pm pm
Air Temperature
MRT
Air Velocity
Fig. 7: Graph of Coastal Area Recapitulation Data(1).
Relative Humidity
Activity
Clothing
19
Hermawan et al, 2015 /Journal Of Applied Sciences Research 11(8), May, Pages: 13-28
temon
temon
temon
marifah
marifah
puji
marifah
puji
puji
harto
harto
agus
harto
agus
agus
resti
resti
resti
yudho
yudho
yudho
120.00
100.00
80.00
60.00
40.00
20.00
0.00
7 12 5 7 12 5 7 12 5 7 12 5 7 12 5 7 12 5 7 12 5
am pm pm am pm pm am pm pm am pm pm am pm pm am pm pm am pm pm
Air Temperature
MRT
Air Velocity
Relative Humidity
Activity
Clothing
Fig. 8: Graph of Coastal Area Recapitulation Data(2).
ludin
ludin
ludin
ropiah
ropiah
ropiah
ninda
ninda
ninda
nariyah
nariyah
nariyah
100.00
80.00
60.00
40.00
20.00
0.00
7 am 12 pm 5 pm 7 am 12 pm 5 pm 7 am 12 pm 5 pm 7 am 12 pm 5 pm
Air Temperature
MRT
Air Velocity
Relative Humidity
Activity
Clothing
Fig. 9: Graph of Coastal Area Recapitulation Data (3).
hadi
hadi
hadi
abnah
abnah
abnah
majyudi
majyudi
majyudi
mafrifah
mafrifah
mafrifah
maskub
maskub
maskub
sabar
sabar
sabar
jamila
jamila
jamila
35.00
30.00
25.00
20.00
15.00
10.00
5.00
0.00
7 12 5 7 12 5 7 12 5 7 12 5 7 12 5 7 12 5 7 12 5
am pm pm am pm pm am pm pm am pm pm am pm pm am pm pm am pm pm
ET*
SET*
TSENS
DISC
Fig.10: Graph of Coastal Area Recapitulation Data (4).
temon
temon
temon
marifah
marifah
marifah
puji
puji
puji
harto
harto
harto
agus
agus
agus
resti
resti
resti
yudho
yudho
yudho
40.00
35.00
30.00
25.00
20.00
15.00
10.00
5.00
0.00
7 12 5 7 12 5 7 12 5 7 12 5 7 12 5 7 12 5 7 12 5
am pm pm am pm pm am pm pm am pm pm am pm pm am pm pm am pm pm
ET*
SET*
TSENS
DISC
Fig. 11: Graph of Coastal Area Recapitulation Data (5).
20
Hermawan et al, 2015 /Journal Of Applied Sciences Research 11(8), May, Pages: 13-28
ludin
ludin
ludin
ropiah
ropiah
ropiah
ninda
ninda
ninda
nariyah
nariyah
nariyah
40.00
30.00
20.00
10.00
0.00
7 am 12 pm 5 pm 7 am 12 pm 5 pm 7 am 12 pm 5 pm 7 am 12 pm 5 pm
ET*
SET*
TSENS
DISC
Fig. 12: Graph of Coastal Area Recapitulation Data (6).
hadi
hadi
abnah
abnah
abnah
majyudi
majyudi
majyudi
mafrifah
mafrifah
mafrifah
maskub
maskub
maskub
sabar
sabar
sabar
jamila
jamila
jamila
100.00
80.00
60.00
40.00
20.00
0.00
-20.00
Tneutral Humphreys
Tneutral Auliciems
To
Nilai AMV
marifah
PMV PPD PD PS TS
marifah
7 12 5 7 12 5 7 12 5 7 12 5 7 12 5 7 12 5 7 12
am pm pm am pm pm am pm pm am pm pm am pm pm am pm pm am pm
Fig. 13: Graph of Coastal Area Recapitulation Data (7).
temon
temon
temon
marifah
puji
puji
puji
harto
harto
harto
agus
agus
agus
resti
resti
resti
yudho
yudho
yudho
100.00
80.00
60.00
40.00
20.00
0.00
-20.00
7 12 5 7 12 5 7 12 5 7 12 5 7 12 5 7 12 5 7 12 5
am pm pm am pm pm am pm pm am pm pm am pm pm am pm pm am pm pm
PMV
PPD
PD
PS
TS
Tneutral Humphreys
Tneutral Auliciems
To
Nilai AMV
Fig. 14: Graph of Coastal Area Recapitulation Data (8).
100.00
50.00
0.00
nariyah nariyah nariyah ninda
PMV 7 am
PPD 12 pm
PD 5 PS
pm
ninda
ninda
ropiah ropiah ropiah
ludin
ludin
ludin
TS
Nilai AMV
7 am Tneutral
12 pm Humphreys
5 pm 7 am Tneutral
12 pm Auliciems
5 pm 7 amTo 12 pm
5 pm
Fig. 15: Graph of Coastal Area Recapitulation Data (9).
Notes:
MRT = Meant Radiant Temperature
SET* = Standard of Effective Temperature
TSENS=Thermal Sensation Index
DISC=Discomfort
PMV=Predicted Mean Vote
PPD=Predicted Percentage of Dissatisfied
ET* = Effective Temperature
PD=Predicted Percent Dissatisfied
PS=Predicted of Operative Temperature and Air Velocity
TS=Predicted of Thermal Sensation Vote
To=Operative Temperature
21
Hermawan et al, 2015 /Journal Of Applied Sciences Research 11(8), May, Pages: 13-28
Table 3: RespondentProfile in Mountain Area
No
House Specification
Respondent Name
1
River Stone Wall
ginah
2
River Stone Wall
bardi
3
River Stone Wall
Subih yati
4
River Stone Wall
Sumiyati
5
River Stone Wall
Afat
6
River Stone Wall
khusen mubarok
7
River Stone Wall
Murdi
8
River Stone Wall
wahyudin
9
River Stone Wall
surati
10
River Stone Wall
radianto
11
River Stone Wall
kliem
12
River Stone Wall
sigadno
13
Wooden Wall
Rival
14
Wooden Wall
mujahya
15
Wooden Wall
sunarto
16
Wooden Wall
Ginah
17
Wooden Wall
umalah
18
Wooden Wall
aminudin
19
Wooden Wall
arifin
20
Wooden Wall
madasir
21
Wooden Wall
painah
Job
Farmer
Farmer
Farmer Worker
Farmer Worker
Farmer Worker
Farmer Worker
Farmer
Farmer Worker
Farmer
Farmer
Farmer
Farmer
Student
Farmer
Farmer
Farmer
Farmer Worker
Farmer Worker
Farmer Worker
Farmer Worker
Farmer Worker
Long Settled
50 years
75 years
40 years
40 years
17 years
14 years
25 years
24 years
22 years
42 years
40 years
18 years
21 years
18 years
40 years
35 years
58 years
30 years
28 years
55 years
52 years
Sex
Female
Female
Female
Female
Male
Male
Male
Male
Female
Male
Female
Male
Male
Male
Female
Female
Female
Male
Male
Male
Female
sunarto
sunarto
sunarto
mujahya
mujahya
rival
mujahya
rival
rival
khusen
khusen
khusen
afat
afat
afat
Sumiyati
Sumiyati
Sumiyati
Subih yati
Subih yati
Subih yati
180
160
140
120
100
80
60
40
20
0
7 12 5 7 12 5 7 12 5 7 12 5 7 12 5 7 12 5 7 12 5
am pm pm am pm pm am pm pm am pm pm am pm pm am pm pm am pm pm
age
height
weight
Du Bois (Adu)
Fig. 16: Graph of Mountain Area Respondent Profile (1).
arifin
arifin
aminudin
aminudin
umalah
umalah
umalah
bardi
bardi
bardi
ginah
ginah
ginah
murdi
murdi
murdi
ginah
ginah
ginah
180
160
140
120
100
80
60
40
20
0
7 12 5 7 12 5 7 12 5 7 12 5 7 12 5 12 5 12 5
am pm pm am pm pm am pm pm am pm pm am pm pm pm pm pm pm
age
height
weight
Fig. 17: Graph of Mountain Area Respondent Profile (2).
Du Bois (Adu)
22
Hermawan et al, 2015 /Journal Of Applied Sciences Research 11(8), May, Pages: 13-28
tuyono
sigadno
sigadno
kliem
kliem
kliem
radianto
radianto
radianto
painah
painah
painah
madasir
madasir
surati
madasir
surati
surati
wahyudin
wahyudin
wahyudin
180
160
140
120
100
80
60
40
20
0
7 12 5 7 12 5 7 12 5 7 12 5 7 12 5 7 12 5 7 12 5
am pm pm am pm pm am pm pm am pm pm am pm pm am pm pm am pm pm
age
height
weight
Du Bois (Adu)
Fig. 18: Graph of Mountain Area Respondent Profile (3).
sunarto
sunarto
sunarto
mujahya
mujahya
mujahya
rival
rival
rival
khusen
khusen
khusen
afat
afat
afat
Sumiyati
Sumiyati
Sumiyati
Subih yati
Subih yati
Subih yati
100.00
80.00
60.00
40.00
20.00
0.00
7 12 5 7 12 5 7 12 5 7 12 5 7 12 5 7 12 5 7 12 5
am pm pm am pm pm am pm pm am pm pm am pm pm am pm pm am pm pm
Air Temperature
MRT
Air Velocity
Relative Humidity
Activity
Clothing
Fig. 19: Graph of Mountain Area Recapitulation Data (1).
arifin
arifin
aminudin
aminudin
umalah
umalah
umalah
bardi
bardi
bardi
ginah
ginah
ginah
murdi
murdi
murdi
ginah
ginah
ginah
100.00
80.00
60.00
40.00
20.00
0.00
7 12 5 7 12 5 7 12 5 7 12 5 7 12 5 12 5 12 5
pm pm amMRT
pm pmAiramVelocity
pm pm am
pm Humidity
pm am pm Activity
pm pm pm
pm pm
Air am
Temperature
Relative
Clothing
Fig. 20: Graph of Mountain Area Recapitulation Data (2).
tuyono
sigadno
sigadno
kliem
kliem
kliem
radianto
radianto
radianto
painah
painah
painah
madasir
madasir
madasir
surati
surati
surati
wahyudin
wahyudin
wahyudin
100.00
80.00
60.00
40.00
20.00
0.00
7 12 5 7 12 5 7 12 5 7 12 5 7 12 5 7 12 5 7 12 5
am pm pm am pm pm am pm pm am pm pm am pm pm am pm pm am pm pm
Air Temperature
MRT
Air Velocity
Fig. 21: Graph of Mountain Area Recapitulation Data (3).
Relative Humidity
Activity
Clothing
23
Hermawan et al, 2015 /Journal Of Applied Sciences Research 11(8), May, Pages: 13-28
sunarto
sunarto
sunarto
mujahya
mujahya
mujahya
rival
rival
rival
khusen
khusen
khusen
afat
afat
afat
Sumiyati
Sumiyati
Sumiyati
Subih yati
Subih yati
Subih yati
25.00
20.00
15.00
10.00
5.00
0.00
-5.00
7 12 5 7 12 5 7 12 5 7 12 5 7 12 5 7 12 5 7 12 5
am pm pm am pm pm am pm pm am pm pm am pm pm am pm pm am pm pm
ET*
SET*
TSENS
DISC
Fig. 22: Graph of Mountain Area Recapitulation Data (4).
arifin
arifin
aminudin
aminudin
umalah
umalah
umalah
bardi
bardi
bardi
ginah
ginah
ginah
murdi
murdi
murdi
ginah
ginah
ginah
30.00
25.00
20.00
15.00
10.00
5.00
0.00
-5.00
7 12 5 7 12 5 7 12 5 7 12 5 7 12 5 12 5 12 5
am pm pm am pm pm am pm pm am pm pm am pm pm pm pm pm pm
ET*
SET*
TSENS
DISC
Fig. 23: Graph of Mountain Area Recapitulation Data (5).
tuyono
sigadno
sigadno
kliem
kliem
kliem
radianto
radianto
painah
radianto
painah
painah
madasir
madasir
madasir
surati
surati
surati
wahyudin
wahyudin
wahyudin
30.00
25.00
20.00
15.00
10.00
5.00
0.00
-5.00
7 12 5 7 12 5 7 12 5 7 12 5 7 12 5 7 12 5 7 12 5
am pm pm am pm pmET*
am pmSET*
pm am TSENS
pm pm amDISC
pm pm am pm pm am pm pm
Fig. 24: Graph of Mountain Area Recapitulation Data (6).
sunarto
sunarto
sunarto
mujahya
mujahya
mujahya
rival
rival
rival
khusen
khusen
khusen
afat
afat
afat
Sumiyati
Sumiyati
Sumiyati
Subih yati
Subih yati
Subih yati
120.00
100.00
80.00
60.00
40.00
20.00
0.00
-20.00
7 12 5 7 12 5 7 12 5 7 12 5 7 12 5 7 12 5 7 12 5
am pm pm am pm pm am pm pm am pm pm am pm pm am pm pm am pm pm
PMV
PPD
PD
PS
TS
Tneutral Humphreys
Fig. 25: Graph of Mountain Area Recapitulation Data (7).
Tneutral Auliciems
To
Nilai AMV
24
Hermawan et al, 2015 /Journal Of Applied Sciences Research 11(8), May, Pages: 13-28
arifin
arifin
aminudin
aminudin
umalah
umalah
umalah
bardi
bardi
bardi
ginah
ginah
ginah
murdi
murdi
murdi
ginah
ginah
ginah
120.00
100.00
80.00
60.00
40.00
20.00
0.00
-20.00
7 12 5 7 12 5 7 12 5 7 12 5 7 12 5 12 5 12 5
am pm pm am pm pm am pm pm am pm pm am pm pm pm pm pm pm
PMV PPD PD PS TS
Tneutral Humphreys
Tneutral Auliciems
To
Nilai AMV
Fig. 26: Graph of Mountain Area Recapitulation Data (8).
tuyono
sigadno
sigadno
kliem
kliem
kliem
radianto
radianto
radianto
painah
painah
painah
madasir
madasir
madasir
surati
surati
surati
wahyudin
wahyudin
wahyudin
120.00
100.00
80.00
60.00
40.00
20.00
0.00
-20.00
7 12 5 7 12 5 7 12 5 7 12 5 7 12 5 7 12 5 7 12 5
am pm pm am pm pm am pm pm am pm pm am pm pm am pm pm am pm pm
PMV PPD PD PS TS
Tneutral Humphreys
Tneutral Auliciems
To
Nilai AMV
Fig. 27: Graph of Mountain Area Recapitulation Data (9).
The total data collected were 115 sets and after
processing the data, by having the 18 variables time
115 set of data, a total of 2070 data were collected in
the end.
In average, the respondents at tropical coast
residential houses have the height of 164.30 cm,
weight 56.55 kg, the youngest age is 17 years old and
the oldest one is80 years old. Whereas at tropical
mountain residential house, in average, the people’s
height is 157.39 cm, weight 43.54 kg, with the
youngest age is 10 years old and the oldest one is75
years old. The facts affected the skin surface area
which could feel the thermal condition. The wide of
thermal condition area was calculated with the
formula below:
Du Bois(Adu) = 0.202 W0.425H0.725
The W refers to weight, whereas the H refers to
height. The result of Dubois calculation provides the
average value of the occupants’ body skin surface
that is 45.19 cm2 and for the occupants in tropical
mountain area is 43.54 cm2. The value showed that
the difference was not much since Indonesian
people’s body posture is generally not different from
one area to another.
The air temperature was different between the
residential house in tropical coast area and mountain
area. In average, the difference is 6.3 oC. The
minimum temperature has the difference of 4.5 oC
and the maximum temperature has the difference of
8.7oC. In average, the globe temperature has the
difference of 6.4oC. The minimum globe temperature
has the difference of 6.0oC, and the maximum one
has the difference of 8.6oC. The wind speed (v)
showed that it was not much different. The difference
is only less than 0.2m/s. The relative humidity could
show big difference as it is influenced by the
moisture content in the area. In average, the relative
humidity is not really different that is 1.53%. The
minimum relative humidity is 3.0% where as the
maximum relative humidity is 8.0%.
As for metabolism, or the activity done, was the
same. It was sitting down together and having a
conversation. When the questionnaire was given to
the respondents, the researcher assisted the occupants
and the activities were only sitting down and having
conversation together. The value of it is 1.2met.
25
Hermawan et al, 2015 /Journal Of Applied Sciences Research 11(8), May, Pages: 13-28
The clothes worn were various. In average, the
value of clo (cloth) worn by the occupants in tropical
coast area is 0.47 clo with the minimum value of
0.24 clo and maximum value of 0.84 clo. In tropical
mountain area, the average value of clo is 0.62, with
Table 4: Data Recapitulation.
Type of Area
Type of Data
Tropical Coast
Average
Minimum
Maximum
Tropical Mountain
Average
Minimum
Maximum
Notes:
Du Bois = skin surface area (cm2)
Ta = air temperature (oC)
Tg = globe temperature (oC)
V = wind speed (m/s)
Age
17
80
10
75
Height
164.30
157.00
175.00
157.39
110.00
170.00
minimum value of 0.27 clo and maximum value of
0.98 clo. The difference of the average value of clo
from both area is 0.14 clo, whereas the difference of
minimum clo is 0.03 clo and the difference of
maximum clo value is 0.14 clo (Table 4).
Weight
56.55
40.00
80.00
55.61
32.00
72.00
The effective temperature is quite different
between both area that is 7.35oC for the average ET*
value, whereas the difference in the minimum ET*
value is 5.80oC and the difference in the maximum
ET* value is 10.30oC. The temperature calculation of
SET* has big difference in its minimum temperature
value, that is 14.70oC, whereas the difference in
average SET* value is 6.60oC and the difference in
the maximum SET* value is 10.3oC
The average PMV value in tropical coast area is
+0.85. It means that it was predicted that the thermal
comfort that the occupants felt rather warm, whereas
the PMV value in the tropical coast area is -0.70,
which indicates that the occupants experienced the
rather cool thermal comfort. The minimum PMV
value of 0.00 in the tropical coast area can be implied
that in a certain period, it was predicted that the
occupants felt comfortable or neutral. On the other
hand, in the tropical mountain area, the maximum
temperature value is +0.08, which indicates that the
occupants would feel nearly comfortable or neutral in
some certain moment.
The PMV value has quite big difference in
minimum PMV values that are 2.45 for the minimum
PMV value, 2.37 for the maximum PMV value and
1.66 for the average PMV value. It indicates that the
occupants felt different thermal condition since the
environment that established the thermal condition
was also different. The average value of PPD has the
difference of 10.10% which indicates that the
acceptance level of the occupants towards the
thermal condition for both areas is as much as
10.10% from the total occupants.
The neutral temperature is viewed from 2
theories that are Humphrey’s theory and Auliciem’s
theory. Based on the Humphrey neutral temperature,
it can be stated that there is no difference. However,
when it refers to Auliciems neutral temperature, there
is a difference in the average neutral temperature
value that is 2.92oC. In addition, there are differences
in the minimum neutral temperature, that is 1.4oC
and the maximum neutral temperature that is 4.2oC.
Du Bois
45.19
38.91
55.00
43.54
26.61
50.89
Ta
27.67
23.80
32.70
21.37
19.30
24.00
Tg
27.17
23.10
31.70
20.73
17.10
23.10
V
0.12
0.10
0.30
0.10
0.10
0.10
RH
81.44
66.00
96.00
79.92
63.00
88.00
met
1.2
1.2
1.2
1.2
1.2
1.2
Clo
0.47
0.24
0.84
0.62
0.27
0.98
RH = relative humidity (%)
met = metabolism (met)
clo = cloth (clo)
The calculation of operative temperature (T o) is
based on the formula of the average between air
temperature and globe temperature. The average To
value is obtained as much as 6.29oC, whereas the
difference in minimum To value is 4.9oC and the
difference in maximum To value is 8.9 oC. Operative
temperature is used by many researchers in
predicting thermal comfort. As there are differences
in the value of operative temperature; therefore,
acceptance level towards thermal in each area can
also be observed [22].
The Actual Mean Vote (AMV) value was
obtained by collecting the vote of subjective data
through questionnaire. The concept of AMV
emerged since there was refusal found on the concept
of PMV. The average AMV value between tropical
coast area and mountain area has the difference of
1.01 point. This indicates that the occupants in
tropical coast area felt and perceived hotter thermal
comfort in comparison to the mountain area. There
was no difference in minimum and maximum AMV
value since the occupants of both the tropical coast
and mountainous area had experienced cold and hot
condition.
The AMV value in tropical coast area has the
average value of +0.5.It means from the result of the
survey that the occupants generally felt thermal
comfort from neutral to rather warm level, whereas
in tropical mountain area, the AMV value is -0.52,
which indicates that the occupants felt thermal
comfort from neutral to rather cool level.
The difference between PMV average value and
AMV average value either in coast area or in
mountain area was not so much because the subjects
did not move from the area of thermal sensation that
was determined (table 5). In the coast area, the
occupants still felt the neutral to rather warm thermal
comfort, whereas in mountain area, the occupants felt
neutral to rather cool thermal comfort. However,
there was a difference between the value of PMV and
AMV. It showed that in the coast area there was a
difference value of +0.35. It indicates that the PMV
26
Hermawan et al, 2015 /Journal Of Applied Sciences Research 11(8), May, Pages: 13-28
model predicted higher than AMV field test result.
Furthermore, in mountain area, a difference in
average value was obtained as much as -0.18 which
Table 5: Data Result Comparison.
Type of Area
Type of Data
ET*
Tropical Coast
Average
28.77
Minimum
24.70
Maximum
34.00
Tropical Mountain
Average
21.42
Minimum
18.90
Maximum
23.70
Note :
ET* = Effective Temperature (oC)
SET* = Standard of Effective Temperature (oC)
PMV = Predicted Mean Vote
SET*
27.98
24.70
33.60
21.38
10.00
24.70
PMV
+0.85
0.00
+2.81
-0.70
-2.36
+0.08
PMV
+0.85
0.00
+2.81
-0.70
-2.36
+0.08
PPD
30.64
0.00
92.00
20.54
5.00
90.00
Tn(humphreys)
21.90
21.90
21.90
21.90
21.90
21.90
Tn(auliciems)
24.53
22.00
27.00
21.60
20.60
22.80
To
27.35
23.50
32.20
21.06
18.60
23.30
AMV
+0.50
-3.00
+3.00
-0.52
-3.00
+3.00
PPD = Predicted of Percentage Dissatisfied (%)
Tn(humphreys) = Neutral temperature Humphreys (oC)
Tn(auliciems) =Neutral temperature Auliciems(oC)
There was also quite significant difference in
minimum value in hot area, where it was predicted
that there was no occupant who would feel rather
cool, cool or cold at a certain hour (PMV value = 0).
However, from the AMV field test, a minimum value
of -3.00 obtained indicates that some occupants felt
cold. The maximum value, on the other hand,
indicates predictably that there would be occupants
Table 6:The Difference of PMV and AMV.
Type of Area
Type of Data
Tropical Coast
Average
Minimum
Maximum
Tropical Mountain
Average
Minimum
Maximum
indicates the PMV model was predicted lower than
AMV field test result.
who would feel hot at the maximum level. In coast
area, a PMV value is obtained as much as +2.81,
which indicates it was nearly hot although it was in
the category of rather warm to hot area. On the other
hand, the AMV got the result of maximum value that
is +3.00 which indicates that the occupants felt hot.
From the result of PMV and AMV, there is not much
difference found (Table 6).
Meaning
Neutral to rather warm
Neutral/comfortable
Warm to hot
Neutral to rather cool
Cool to cold
Neutral to rather warm
There was a difference found in minimum value
of PMV and AMV for mountain area although it was
not considered much. The minimum PMV value
obtained is -2.36, which indicates that the occupants
were predicted to feel the thermal level of cool to
cold (nearly cool), whereas the AMV minimum
value is -3.00 which indicates that the occupants felt
cold. The PMV predicted 0.64 points higher than
AMV. This result is likely fit to another research
[23]. Sugini also conducts research with the same
results and produces a model that corrects PMV
equation isPMVTAP=PMV+ Ŷvorpmv [24].
The maximum PMV value is +0.08. It was
predicted that the occupants would experience
thermal comfort level from neutral to rather warm in
a certain hour but they never felt warm or hot. The
AMV maximum value on the other hand got the
value of +3.00 which indicates that the occupants
experienced hot thermal comfort. At the maximum
value, PMV was predicted lower than AMV field test
result.
Conclusion:
The mean value of PMV in tropical coastal area
is +0.85 which explains that the occupants felt
‘neutral’ tending to ‘warm enough’. The thermal
sensation ‘warm enough’ possesses +1.00 values so
the value +8.85 approaches the ‘warm enough’
sensation. Tropical mountainous area owns PMV
AMV
+0.50
-3.00
+3.00
-0.52
-3.00
+3.00
Meaning
Neutral to rather warm
Cold
Hot
Neutral to rather cool
Cold
Hot
Difference
+0.35
+3.00
+0.19
-0.18
-0.64
+2.92
value o f -0.70. It means that the occupants in
tropical mountainous had ‘neutral’ tending to ‘cool
enough’ thermal sensation. The PMV value for both
areas is quite different considering the thermal
variables especially temperature which hasinverse
proportion.
The tropical coastal area has AMY + 050 which
reveals that the occupants had thermal sensation in
the middle ‘neutral’ and ‘warm enough’, while
tropical mountainous area has AMV -0.52. It means
that the occupants possessed the middle of ‘neutral’
and ‘cool enough’. This thermal constituted the result
of field study that could be collected from the
questionnaire based on 7point scale ASHRAE. The
value of AMV in tropical coastal area is also inverted
proportion with the value of mountain area because
thermal variables are different between the two areas
of study.
Referring to the result of PMV, it was predicted
that the occupants in tropical coast area would feel
comfortable at a certain hour, whereas those who
lived in mountain area only nearly felt comfortable at
a certain hour. The result of PMV had some
differences with AMV, either in tropical coast area or
in tropical mountain area. The PMV predicted the
higher (hotter) thermal comfort level in comparison
to AMV for tropical coast area, but for tropical
mountain area, the PMV predicted lower (cooler)
thermal comfort level than AMV. The prediction of
PMV, which was estimated higher in tropical coast
area, was considered the same as the result of another
study. However, the result of PMV, which predicted
lower value than AMV result in tropical mountain
area, was considered something different. Having
known about this, some other researches in different
mountain areas are considered necessarily to be
conducted as the result could be accepted by many
other areas.
Acknowledgments
The authors would like to axknowledge Dr.
Sugini who gives help and supports in my Research.
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