Computer Applications in Environmental Sciences and Renewable Energy
Simulation Analysis of Weather Effect on Cooling Capacity and Cooling
Load for a Small Building Cooled by Solar Adsorption
Air-Conditioning System
ALKHAIR M. A., M. Y. SULAIMAN, K. SOPIAN, C. H. LIM, S. MAT, E. SALLEH
Solar Energy Research Institute (SERI)
UniversitiKebangsaan Malaysia (UKM)
43600 Bangi, Selangor
MALAYSIA
phd.alkhair@yahoo.com
Abstract: -This study indicates the analysis of the weather data effect on a small building cooled by direct solar
adsorption air-conditioning system. The weather data of Malaysia was used in the simulation, with a cooling
load and its variation due to the solar radiation change. TRNSYS simulation was used in this study with the
assistance of HAP 4.6 software. With a cooling capacity of 3.5 kW for the adsorption system, the maximum
cooling load recorded was 3.1 kW. The results of the study show that the weather data plays a major rule on the
cooling load and the performance of the adsorption cooling system.
Key-Words: -Solar Energy, Weather Effect, Cooling Capacity, Cooling Load, Adsorption System, Simulation.
any building are estimated based on the solar
radiation at a clear sky[7]. So that, these factors
considered the maximum solar heat gain, with the
ambient temperature are adopted in the cooling
load determination and in cooling system
design[8]. The analysis of the building using the
heat transfer approach was considered as very
complex understanding, since the simulated data
must be between the design parameters and the
change in weather data at the same time[9].
The cooling loads are the energy needed to be
removed from the building by the cooling system to
provide the desired level of comfort. The right size
of the cooling system starts with the understanding
of the cooling loads on the building[10]. For the
estimating of the cooling loads, the unsteady state
process has to be considered, as the peak cooling
load occurs along the day time and the
environmental conditions also vary throughout the
same period according to the solar radiation
change[11]. The total cooling load of the building
consists of heat transferred through the building
which contains (wall, roof, floor, window, door,
etc.), and the heat generated by the occupation of
the space and lights or any other equipment used
inside the building[12]. The percentage of external
load to the internal load varies with the building
design, the weather data, and the type of the
building. The total cooling load of any building
consists
of
sensible
and
latent
load
components[13]. This paper studies the influences
of weather data in climatic region on the cooling
1 Introduction
The climate of any location tends the influence of
the building design according to some parameters
such as the shape, material, position, and weather
data[1]. The main strategy of any building design is
to reduce the cooling capacity requirements during
the hot weather period[2]. The major parameters
that affecting the cooling load of the building are
the dry bulb and wet bulb temperatures, the solar
radiation, and humidity[3]. The solar radiation and
the wet bulb temperature are important to the
calculations of heat gain and latent heat load,
respectively[4]. Hence the cooling capacity of any
refrigeration or cooling system designed to cover
the maximum cooling load of the building, and
then the requirement of any building is directly
proportional to the weather effect[5]. In
conventional refrigeration and cooling systems
both includes the estimation of the peak load at a
specific period based on the maximum cooling load
at the same period. The weather data which
represents the climatic conditions are utilized in the
calculation of the cooling load, therefore, for solar
driven air conditioning and refrigeration systems,
the performance of the system depends on the
weather data condition, and then the relationship
between the cooling load and the cooling capacity
is directly related to the weather data[6]. According
to the American Society of Heating, Refrigerating,
and Air Conditioning Engineers (ASHRAE), the
design factors for the cooling load calculations of
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Computer Applications in Environmental Sciences and Renewable Energy
September with an average monthly solar radiation
of 735 Wh/m2.
load and cooling capacity for a small building in
Bangi, Malaysia.
2 Adsorption system
Solar radiation is a sustainable and clean source of
energy in the world. Many technologies utilize this
source of free energy to compensate the increasing
in fuel price[14]. One of these technologies is the
solar cooling, which is compatible with this type of
free energy source. The solar cooling system
introduces processes that are benign to the
environment due to its zero rate of ozone depletion
potential (ODP) and low effect on global warming
potential (GWP), compared to other standard
technology such as vapor compression[15]. In this
study the model of two adsorption beds solar
cooling system was carried out, used for the
purpose of air conditioning in small building with a
capacity of one refrigeration ton (i.e. 3.5 kW
cooling capacity). Figure (1) below represents the
schematic diagram of the adsorption system
connected to the building.
Figure 2. Annual global solar radiation
4 Simulation process
In order to obtain the cooling capacity of the solar
adsorption cooling system, a simulation program
was developed using MATLAB. The simulation
results indicate that the maximum cooling capacity
of the system reach 3.6 kW at various hot water
temperatures in different solar radiation. figure (3)
below represents the flow chart of the simulation
for the whole system which consists of weather
data analysis, solar adsorption cooling system, and
the analysis of the cooling load.
Figure 1. Schematic diagram of the system
From the figure above, the main source of energy
used for the adsorption system is the evacuated
tube collectors with an area of 11m2. The evacuated
tube collectors connected to the adsorption system
by insulated pipes, and then the cooling system is
connected to the building which represents the
cooling load.
3 Weather data
The accuracy of the weather data plays an
important role in the solar cooling load and solar
cooling systems, both the ambient temperature and
the solar radiation are important[16]. The weather
data for the year 2013 for the location Bangi,
Selangor, Malaysia has been used. Figure (2) below
shows the annual global solar radiation with an
average solar hours of 12/day. The maximum solar
radiation occurred in February, March, and
ISBN: 978-960-474-370-4
Figure 3. Simulation flowchart of the system
157
Computer Applications in Environmental Sciences and Renewable Energy
Table 2: Maximum and minimum weather data
days.
Form the figure above, the simulation was done in
three processes or levels; the first stage was
considering the simulation of the weather data
collected in the site with the analysis of that data to
minimize it to calculate the average annual typical
metrological year (TMY) for that location in one
year. The second was to simulate the adsorption
system as illustrated above using the developed
simulation program that consist all the thermal
equilibrium equations handling the solar adsorption
cooling system performance. The results from these
governing equations were estimated in transient
state not in steady state mode, according to
different solar radiation and ambient temperature
within the time for the whole period. The third
stage was to simulate the cooling load of the small
building with the weather data also. The fluctuating
of weather data records lead to a very complicated
analysis of the cooling load, where the cooling load
was analyzed using the Hourly Analysis Program
HAP v.4.6.
All the above simulation was combined together in
one simulation program using the TRNSYS
software, where the simulation program of the
adsorption system connected to the cooling load,
both influenced by the weather data were combined
in one simulation process.The American Society of
Heating, Refrigerating and Air Conditioning
Engineers (ASHRAE), recommended the dry bulb
temperature with a frequency of 2.5% and its
simultaneous wet bulb temperature design
procedure for HVAC applications [7]. In this study,
the calculations of the cooling load were estimated
under the assumptions and design parameters as
shown in table (1), while table (2) indicates the data
in which the maximum and minimum dry bulb
temperatures and solar irradiation occur during one
year period.
Highest DryBulb
Temperature
January
Jan 25, 1500
February
Feb 21, 1400
March
Mar 26, 1500
April
Apr 1, 1400
May
May 21, 1400
June
Jun 24, 1400
July
Jul 3, 1400
August
Aug 1, 1500
September Sep 29, 1300
October
Oct 10, 1200
November Nov 6, 1500
December Dec 27, 1400
Month
Remarks
34.5oC
25.6oC
0.85
Cloudy climate
0.2
Grass ground
1.385 W/m/K
2
15 m
Education
1
ISBN: 978-960-474-370-4
Minimum
Total
Solar
Jan 13
Feb 8
Mar 12
Apr 27
May 19
Jun 26
Jul 12
Aug 25
Sep 17
Oct 11
Nov 2
Dec 16
The incentive study is to simulate the effect of the
weather data on a small building cooled by a solar
adsorption air-conditioning system having a
cooling capacity of 3.5kW. The average annual
cooling load for the building was simulated as
shown in figure (4). It is obvious found that the
cooling load of the building is almost at the same
numerical value with a maximum of 3.1 kW. The
fluctuating in the cooling load as shown in the
figure indicated that the weather data effect is very
important as the change in the solar radiation and
the ambient temperature both affecting on the
calculation of the external and internal cooling
loads.
3.0
2.5
kW
2.0
1.5
1.0
0.5
0.0
360
352
344
336
328
320
312
304
296
288
280
272
264
256
248
240
232
224
216
208
200
192
184
176
168
160
152
144
136
128
120
112
104
96
88
80
72
64
56
48
40
32
24
16
8
Value
Maximum
Total
Solar
Jan 25
Feb 3
Mar 25
Apr 2
May 8
Jun 2
Jul 7
Aug 31
Sep 7
Oct 5
Nov 6
Dec 24
5 Results and discussion
Day of Year
Figure 4. Annual cooling load values
Table 1: cooling load design parameters
Design Parameters
Weather
Design DB Temperature
Design WB Temperature
Atmospheric
Clearness
Number
Average
ground
reflectiveness
Soil conductivity
Building
Area
Space usage
No. of level
Lowest DryBulb
Temperature
Jan 30, 0600
Feb 3, 0600
Mar 5, 0600
Apr 29, 0600
May 7, 0300
Jun 29, 0500
Jul 14, 0600
Aug 25, 0500
Sep 1, 0300
Oct 12, 0500
Nov 6, 0500
Dec 3, 0500
Figures (5 to 16) respectively, represent the
monthly cooling load with the cooling capacity of
the system. The blue line represents the cooling
capacity of the solar adsorption system, while the
black line represents the cooling load of the
building. It is found that all the profiles of monthly
cooling load for the whole year started from 12:00
AM with very low value and then start increasing
as the sun rises with a parallel to the cooling
capacity profile except on the peak period of the
day as the solar radiation and the ambient
temperature were at their maximum values.
According to these profiles, the maximum cooling
load which will be above the cooling capacity of
the system occurred in January, February, and
Muddy ground
Computer lab
Ground floor
158
Computer Applications in Environmental Sciences and Renewable Energy
December, as these months in the year 2013 were
recorded as the warmest period in the year since
many years ago.
June 2013
January 2013
July 2013
February 2013
August 2013
March 2013
September 2013
April 2013
October 2013
May 2013
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Computer Applications in Environmental Sciences and Renewable Energy
stages consists the simulation of the weather data,
the simulation of the cooling load of the building,
and the simulation of the adsorption cooling
system. Each stage or process was connected to the
other two processes to illustrate the relationship
between them. The results of the simulation
indicated that the cooling capacity of the adsorption
system can carry out the cooling load of the
building except for three months, where the cooling
load was almost equal or above the value of the
cooling capacity of the system.
November 2013
December 2013
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Figures 5-16. Monthly cooling load and cooling
capacity of the system
Cooling Load
Cooling Capacity
3.2
3.1
3
2.9
2.8
2.7
2.6
2.5
2.4
2.3
2.2
2.1
2
Cooling Capacity [kW]
2.8
2.7
2.6
2.5
2.4
2.3
2.2
2.1
2
1.9
1.8
1.7
1.6
1.5
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
NOV
DEC
Cooling Load [kW]
Figure (17) below shows the annual average
cooling load and cooling capacity calculated from
the simulation. Both curves varied according to the
solar radiation from the weather data change during
the year.
Month of the Year
Figure 17.Annual average rate of cooling load and
cooling capacity
6 Conclusion
This paper investigated the simulation of weather
database change and its influence on the cooling
load of a small building in tropical climate cooled
by using solar adsorption air-conditioning system
having a cooling capacity of 3.5 kW. The monthly
changed in the cooling load for the year 2013 in the
location of Bangi, Selangor, Malaysia was
simulated according to the typical metrological
year (TMY) of the same location. The simulation
carried out in this study was divided into three
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Computer Applications in Environmental Sciences and Renewable Energy
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