Investigation of the effect of weather c
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International Journal of Sustainable
Energy
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Investigation of the effect of weather
conditions on solar radiation in Brunei
Darussalam
a
a
M. G. Yazdani , M. A. Salam & Q. M. Rahman
b
a
Facult y of Engineering, Inst it ut Teknologi Brunei, Bandar Seri
Begawan, Brunei Darussalam
b
Depart ment of Elect rical and Comput er Engineering, Universit y
of West ern Ont ario, London, ON, Canada N6A 5B9
Published online: 13 Oct 2014.
To cite this article: M. G. Yazdani, M. A. Salam & Q. M. Rahman (2014): Invest igat ion of t he ef f ect
of weat her condit ions on solar radiat ion in Brunei Darussalam, Int ernat ional Journal of Sust ainable
Energy, DOI: 10. 1080/ 14786451. 2014. 969266
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International Journal of Sustainable Energy, 2014
http://dx.doi.org/10.1080/14786451.2014.969266
Investigation of the effect of weather conditions on solar
radiation in Brunei Darussalam
M.G. Yazdania , M.A. Salama and Q.M. Rahmanb∗
a Faculty of Engineering, Institut Teknologi Brunei, Bandar Seri Begawan, Brunei Darussalam;
b Department of Electrical and Computer Engineering, University of Western Ontario, London, ON,
Canada N6A 5B9.
(Received 20 June 2014; accepted 16 September 2014)
The amount of solar radiation received on the earth’s surface is known to be highly influenced by the
weather conditions and the geography of a particular area. This paper presents some results of an investigation that was carried out to find the effects of weather patterns on the solar radiation in Brunei
Darussalam, a small country that experiences equatorial climate due to its geographical location. Weather
data were collected at a suitable location in the University Brunei Darussalam (UBD) and were compared with the available data provided by the Brunei Darussalam Meteorological Services (BDMS). It
has been found that the solar radiation is directly proportional to the atmospheric temperature while it is
inversely proportional to the relative humidity. It has also been found that wind speed has little influence
on solar radiation. Functional relationships between the solar radiation and the atmospheric temperature,
and between the solar radiation and the relative humidity have also been developed from the BDMS
weather data. Finally, an artificial neural network (ANN) model has been developed for training and
testing the solar radiation data with the inputs of temperature and relative humidity, and a coefficient of
determination of around 99% was achieved. This set of data containing all the aforementioned results may
serve as a guideline on the solar radiation pattern in the geographical areas around the equator.
Keywords: solar energy; temperature; relative humidity; wind speed; ANN; correlation
1.
Introduction
It is crucial to determine the patterns of solar radiation throughout the year as solar technologies
heavily rely on these patterns. Both weather conditions and geography of a location have an
influence on solar radiation pattern as demonstrated in many research findings (Coops, Waring,
and Moncrieff 2000; Rivington et al. 2005; Yorukoglu and Celik 2006; FumitoshiNomiyama,
Murakami, and Murata 2011; Shi et al. 2011; Hill et al. 2012). Yorukoglu and Celik (2006) have
presented a review of the estimation of daily global solar radiation from sunshine duration and
demonstrated the interdependency between solar radiation and weather conditions by studying
the meteorological data.
A detailed evaluation of the performance and characteristic behaviour of two air temperaturebased models and one sunshine duration conversion method of estimating solar radiation for
24 meteorological stations has been carried out in Britain (Rivington et al. 2005). Comparisons
*Corresponding author. Email: qrahman@eng.uwo.ca
© 2014 Taylor & Francis
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M.G. Yazdani et al.
between these methods were made using a fuzzy logic-based multiple-indices assessment system
(Irad) and tests of the temporal distribution of mean errors over a year. It has been demonstrated that the two air temperatures-based method can be a reliable alternative when only air
temperature data are available.
Coops, Waring, and Moncrieff (2000) have developed a set of general equations for estimating
diffuse and direct solar radiation on the basis of mean daily maximum and minimum temperatures, latitude, elevation, slope and aspect. These proposed equations have been tested under
different climatic conditions and the resultant variation of solar radiation based on these different climatic conditions has been demonstrated. In another research work (FumitoshiNomiyama,
Murakami, and Murata 2011), a set of three methods to foresee global solar radiation using
weather forecast has been proposed and verified. An algorithm has been proposed by Shi and
others (Shi et al. 2011) to predict the output of a photovoltaic system based on weather data. For
modelling the solar irradiance, a multilayer feed forward artificial neural network (ANN) has
been used by a group of researchers (López, Batlles, and Tovar-Pescador 2005) with selected
input weather parameters. Furthermore, Assi et al. (2013) presented a Multi-layer PerceptronANN model for predicting global solar radiation (GSR) for some major cities in the United
Arab Emirates (UAE); the authors used the weather data between 1995 and 2006 to train the
neural network, while the data from the year 2007 were used to validate the model. Yao et al.
(2014) have evaluated some existing solar radiation models to estimate the solar radiation in
different weather conditions in Shanghai, China, and then established Monthly Average Daily
Global Solar Radiation (MADGSR) models and Daily Global Solar Radiation (DGSR) models
according to 42 years’ local weather data.
All these above-mentioned studies took into account the weather information and demonstrated that the variations in weather conditions decidedly influence the solar radiation pattern.
Bearing this idea in mind, the authors in this paper present some investigative results on the effect
of temperature, relative humidity and wind speed over the solar radiation pattern and develop a
functional relationship among them. Moreover, due to the fact that ANN techniques predict solar
radiation more accurately in comparison with conventional methods (Yadav and Chandel 2014),
the authors have developed an ANN model using back the propagation algorithm to evaluate the
solar radiation based on some selected weather inputs. The location for the experiments was set in
Brunei Darussalam (Geography and Map of Brunei), a small country that experiences a tropical
rainforest climate of hot and humid weather with heavy rainfall throughout the year. Based on the
weather condition of this country, experimental measurements on weather components have been
carried out for this paper. A discussion on the results obtained for different weather items has
been presented here. Also, the empirical relationship between the solar radiation and the weather
data (temperature and relative humidity) has been verified using the neural network model.
2.
Experimental measurement
Solar energy is generally determined by the radiation received from the sun over a wavelength
ranging from 300 to 4750 nm. Although it is essential to determine the solar radiation pattern
throughout the year, in this study, the experiment was carried out during the months of January
and February only. Since Brunei Darussalam is located near the equator, and the average weather
conditions do not vary much throughout the year, the choice of experimental time frame was
reasonably justified. The experiment was carried out during the midday period because maximum
solar radiation is generally received during that time of the day from a clear sky.
Weather data with mean temperature, relative humidity and air velocity were recorded using a
®
®
calibrated Kestrel 3000 Pocket Weather Meter. This metre has a maximum relative expanded
uncertainty for wind speed, temperature and relative humidity of ±0.60%, ±0.020◦ C and
International Journal of Sustainable Energy
Table 1.
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Date
Measured data for January 2013, from 1.00 pm to 2.00 pm.
Temperature (°C)
Relative humidity (%)
Avg. wind Speed (m/s)
33.1
32.0
31.1
31.4
30.6
29.4
31.7
31.8
29.6
77
68
70
73
72
75
74
75
81
0.8
1.2
1.0
0.9
1.3
1.2
1.4
1.1
0.8
21
22
23
24
26
28
29
30
31
Table 2.
Date
3
Measured data for February 2013, from 1.00 pm to 2.00 pm.
Temperature (°C)
Relative humidity (%)
Avg. Wind Speed (m/s)
31.8
31.4
30.8
30.9
29.4
31.7
31.4
32.1
31.2
30.3
30.7
29.3
32.1
29.2
33.1
32.6
26.4
76
73
73
74
87
75
72
69
70
76
75
73
71
79
63
71
96
0.9
1.2
1.1
1.3
1.2
1.1
1.4
1.2
0.7
0.8
1.0
1.8
1.6
2.4
0.8
1.1
1.7
2
4
5
6
7
9
13
14
16
18
19
20
21
25
26
27
28
±0.50% (within the wind speed range between 3 and 40 m/s), respectively. These three parameters were logged at a suitable location known as the Core in the University Brunei Darussalam
(UBD) on selected days between 21 January and 28 February 2013, during a midday time period
between 1:00 pm and 2:00 pm. The above data items were then collected and compared with
the available data obtained from the BDMS. The data provided by the BDMS were measured in
two different districts of Brunei Darussalam: Kuala Belait (KB) and Brunei International Airport
(BIA). The recorded average data obtained in the Core are shown in Tables 1 and 2, respectively.
®
®
Using a Kestrel 3000 Pocket Weather Meter, each of the data items had been averaged over ten
readings before it was recorded on the table.
3.
Results and discussion
This section provides a complete discussion on the recorded weather data during the months of
January and February. The weather data items include average temperature, relative humidity,
wind speed and the resultant solar radiation.
3.1.
Average temperature
As shown in Table 1, it was observed that the average temperature would fall in the range of a
minimum of 29.4°C to a maximum of 33.1°C with a mean of 31.0°C for the month of January.
4
M.G. Yazdani et al.
34
Average tepmerature (deg. C)
BIA
KB
Core
32
31
30
29
28
27
26
25
21
Figure 1.
22
23
24
25
26
Date
27
28
29
30
31
25
28
Average temperatures for January 2013, from 1:00 pm to 2:00 pm.
36.0
34.0
Average temperature (deg. C)
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33
32.0
30.0
28.0
26.0
BIA
24.0
KB
22.0
Core
20.0
1
Figure 2.
4
7
10
13
16
Date
19
22
Average temperatures for February 2013, from 1:00 pm to 2:00 pm.
High relative humidity was experienced throughout this month. During this time of the year,
the lowest, mean and highest average recorded relative humidity values were 68%, 74% and
81%, respectively. The highest humidity was observed during the rainy days. At the same time
in January, the highest, mean and lowest wind speeds of 1.4, 1.1 and 0.8 m/s, respectively, were
recorded. For the month of February, the recorded highest temperature was still at 33.1°C, but the
lowest was 26.4°C. Due to higher rainfall in this month, higher relative humidity was observed
with highest and lowest readings of 94% and 69%, respectively. The recorded wind speed for this
month ranged from a minimum of 0.7 m/s to a maximum of 2.4 m/s. The average temperatures
in January and February for the three places (BIA, Core and KB) are shown in Figures 1 and 2,
respectively. From Figures 1 and 2, it is observed that the measured data at the Core are higher
than that of KB and BIA. Also, from these two figures, it is seen that the average temperature is
generally warmer in January compared with the average temperature in February.
International Journal of Sustainable Energy
5
28.5
Temperature (deg. C)
27.5
2011
2012
27
26.5
1
2
3
4
5
6
7
8
9
10
11
12
30
31
Months
Figure 3.
Monthly average temperature readings.
90
BIA
85
Agerage relative humidity (%)
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28
KB
Core
80
75
70
65
60
21
22
23
24
25
26
27
28
29
Date
Figure 4.
Average relative humidity for January 2013, from 1:00 pm to 2:00 pm.
Figure 3 plots data for the monthly average temperature for BIA. It can be seen from Figure 3
that the annual average temperature in 2012 was higher than that of the readings for 2011,
which were calculated to be 27.8°C (2012) and 27.6°C (2011), respectively. It is interesting
to observe that the data in Figure 3 follow National Aeronautics and Space Administration’s
(NASA) prediction in their long-term climate warming trend.
3.2.
Relative humidly
The average relative humidity was found to be fluctuating significantly in all three places in the
month of January. The fluctuation was not as significant in the month of February, especially,
during mid-February between the dates 11 and 20 as shown in Figures 4 and 5. It is observed
that the relative humidity in KB is much lower than the other two places during the month of
February. The maximum relative humidity experienced in BIA was recorded at 94%, while the
6
M.G. Yazdani et al.
100
Average relative humidity (%)
80
70
60
50
BIA
40
KB
Core
30
1
4
7
10
13
16
19
22
25
28
Date
Figure 5.
Average relative humidity for February 2013, from 1:00 pm to 2:00 pm.
86
2011
Average relative humidity (%)
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90
84
2012
82
80
78
76
1
2
3
4
5
6
7
8
9
10
11
12
Months
Figure 6.
Monthly average relative humidity.
minimum was recorded at 56%. In KB, those corresponding values were 79.8% and 57.8%,
respectively. The mean relative humidity was found to be at 70.8% in BIA and at 70.4% in
KB for the two months. The monthly average relative humidity percentages for 2011 and 2012
are shown in Figure 6 for BIA. From Figure 6, for both 2011 and 2012, a decreasing trend in
relative humidity is observed from January to May. However, there is a fluctuation during the
mid-year and a rising trend starting from October. The average relative humidity for 2012 was
lower compared with 2011 which were 79.7% and 80.9%, respectively.
3.3.
Wind speed
The average wind speeds for the months of January and February are shown in Figures 7 and 8,
respectively. The average wind speed in January for BIA site was higher compared with the other
International Journal of Sustainable Energy
7
6
Average wind speed (m/s)
BIA
KB
4
Core
3
2
1
0
21
Figure 7.
22
23
24
25
26
Date
27
28
29
30
31
Average wind speeds for January 2013, from 1:00 pm to 2:00 pm.
5
BIA
Average wind speed (m/s)
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5
4
KB
Core
3
2
1
0
1
Figure 8.
4
7
10
13
16
Date
19
22
25
28
Average wind speeds for February 2013, from 1:00 pm to 2:00 pm.
sites. The maximum wind speed was found to be 5 m/s in January as shown in Figure 7. The
minimum was observed around 1 m/s in both January and February as can be seen in Figures 7
and 8. The monthly average wind speeds in BIA for the year 2011 and 2012 are shown in
Figure 9. The maximum and minimum wind speeds for the two years were found to be 5.8 and
4 m/s, respectively. It was also observed that the maximum, minimum and mean variations of
wind speed were 0.9, 0.5 and 0.7 m/s, respectively.
3.4.
Solar radiation
The average solar radiations recorded by the BDMS in KB are shown in Figure 10. The trends
of solar radiation for the year 2011 and 2012 are similar. However, most of the monthly radiation
values for the year 2012 are found to be higher compared with the corresponding values for the
year 2011. The above observation is also supported by the NASA’s findings in the year 2013
8
M.G. Yazdani et al.
7
2011
Average wind speed (m/s)
2012
5
4
3
2
1
0
1
2
3
4
5
6
7
8
9
10
11
12
Months
Figure 9.
Monthly average wind speeds.
6
2011
2012
Average radiation (kW/m2)
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6
5.5
5
4.5
4
1
2
3
4
5
6
7
8
9
10
11
12
Months
Figure 10. Monthly average solar radiation.
from the Goddard Institute for Space Studies (GISS) which stated that, globally, 2012 was the
ninth warmest year.
3.5.
Overall discussion
Collected data at the Core were compared with the data from BIA and KB to investigate their
patterns, similarities and differences. The Core data readings were found to be generally higher
than the data obtained from the BDMS. Since data were collected using a handheld device at
the Core while the data from BIA and KB were collected from the weather stations, the latter is
thought to be more accurate. From Figures 1 and 2, it is observed that all the data items show
a similar trend. Since the solar radiation data were available only from KB, the analysis on the
variation of solar radiation was done using the data from KB.
International Journal of Sustainable Energy
Development of correlation
In general, there is an influence of weather condition on the solar radiation (SR) received on
the earth’s surface. The radiation data obtained from KB are plotted against the temperature
and relative humidity to find the correlations between the solar radiation and the atmospheric
temperature, and between the solar radiation and the relative humidity. From the recorded data
provided by the BDMS, it was observed that the wind speed had little or no influence over the
solar radiation as can be seen in Figures 11 and 12. Therefore, the wind speed was not considered
as a parameter in developing the correlation.
To find the relationship between solar radiations with other two components, solar radiation
data were initially plotted over average temperature and relative humidity as shown in Figures
13 and 14, respectively. Later on, a best-fit polynomial curve was used to get the correlation
between the solar radiation and the atmospheric temperature, and between the solar radiation
5.6
5.4
5.2
SR (kW/m2)
5
4.8
4.6
4.4
4.2
4
0.2
2.2
4.2
6.2
8.2
Wind speed (m/s)
Figure 11.
Avg. solar radiation versus wind speed in 2011.
6
5.5
SR (kW/m2)
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4.
9
5
4.5
4
3.5
3
0.5
0.6
0.7
0.8
Wind speed (m/s)
Figure 12.
Avg. solar radiation versus wind speed in 2012.
0.9
1
Avg. solar radiation (kW/m2)
M.G. Yazdani et al.
6.0
5.8
5.6
5.4
5.2
5.0
4.8
4.6
4.4
4.2
4.0
26.5
y = -0.0042x3 + 0.2443x2 - 3.33x
R² = 0.3285
27.0
27.5
28.0
28.5
Avg. temperature (deg. C)
Figure 13. Avg. solar radiation versus avg. temperature.
6.0
Avg. solar radiation(kW/m2)
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10
5.8
5.6
5.4
5.2
5.0
4.8
4.6
y = -0.0002x3 + 0.0306x2 - 1.0862x
R² = 0.4575
4.4
4.2
4.0
76
77
78
79
80
81
82
83
84
85
Avg. relative humidity (%)
Figure 14. Avg. solar radiation versus avg. relative humidity.
and the relative humidity. These correlations are given as follows:
SR = −0.00042t3 + 0.2443t2 − 3.3t
3
2
SR = −0.0002RH + 0.0306RH − 1.0862RH
(1)
(2)
The best-fit polynomial curve in Figure 13 shows that the solar radiation appears to be directly
proportional to atmospheric temperature, while in Figure 14, with the aid of the best-fit polynomial curve, it is observed that the solar radiation is inversely proportional to relative humidity.
In a similar way, an attempt was made to correlate both the temperature and relative humidity
with the solar radiation by plotting t/RH versus SR data items as shown in Figure 15. The data
generally collapsed into a trend line, as shown by the best-fit power curve in Figure 15. This
trend line can be expressed as
t 0.306
(3)
SR = 7.07
RH
4.1.
ANN model
The ANN model has been developed using back propagation algorithm to find the solar radiation
based on the selected inputs from the raw data. In this model, three layers with two hidden
International Journal of Sustainable Energy
11
6
solar radiation (W/m2)
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y = 7.0701x 0.3065
R² = 0.1066
5.5
5
4.5
4
0.32
0.33
0.34
0.35
0.36
0.37
t/RH
Figure 15.
Variation of solar radiation versus t/RH.
Inputs
Temp.
w11
Hidden layers
w12
Output
SR
w21
RH
w22
Figure 16.
Generalised ANN model.
6
5
4
Actual
3
Predicted
2
1
0
1
Figure 17.
3
Training results for the year 2011.
5
7
9
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12
M.G. Yazdani et al.
neurons and a single output have been considered. Here, the temperature and relative humidity
have been considered as the input parameters and the solar radiation has been considered as the
output parameter. As shown in Figure 16, the Group Method for Data Handling (GMDH) ANN
software has been used for modelling. This ANN model has been used to train and test the solar
radiation pattern for the years 2011 and 2012. During the training phase, eight data sets have
been used, while, four data sets have been used during the testing phase. The predicted output
from each of the training sets has been recorded and plotted as shown in Figures 17 and 18, for
the years 2011 and 2012, respectively. The training processes have converged to the threshold
values of 0.078667 (Year: 2011) and 0.069998 (Year: 2012), using two hidden layers.
For modelling, the GMDH neuron function has been considered as
f (x) = a0 + a1 x1 + a2 x2 + a3 x1 x2 + a4 x1 2 + a5 x2 2
(4)
Figures 17 and 18 demonstrate that the actual and the predicted data items are in good agreement during the training session. The coefficients of determination (R2 ) for the training models
have been found to be 0.997925 and 0.997926 for the years 2011 and 2012, respectively. These
6
5
4
Atual
Predicted
3
2
1
0
1
2
3
4
Figure 18. Training results for the year 2012.
7
6
5
Actual
4
Predicted
3
2
1
0
1
3
Figure 19. Testing results for the year 2011.
5
7
International Journal of Sustainable Energy
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7
6
5
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4
Actual
3
Predicted
2
1
0
1
Figure 20.
2
3
4
Testing results for the year 2012.
high percentages of coefficients of determination indicate that the variation in solar radiation
could be obtained by the selected two weather-input variables.
After training the model, the next phase was to validate the model for its accuracy. In this case,
four data sets have been used for testing the models. The test results were found to be in good
agreement with the actual results, and in this case, the coefficients of determination were found
to be 0.977456 (Year 2011) and 0.984924 (Year: 2012). The test phase data have been plotted in
Figures 19 and 20.
5.
Conclusion
The relative humidity, average temperature and average wind speed have been measured at the
Core site of the UBD and are compared with the published data provided by the BDSM. The
functional relationships between the solar radiation and both the temperature and relative humidity have been proposed using a best-fit polynomial curve. It is also found that the wind speed has
almost no influence on the solar radiation. In addition, a highly accurate ANN model for predicting the solar radiation pattern, based on two inputs, has been developed which has provided
close to 100% coefficients of determination (0.997925 and 0.977456, 0.997926 and 0.984924)
for both training and testing phases for the 2011 and 2012 year data. This model can be used for
future prediction of solar radiation pattern with the selected weather-input variables.
Acknowledgements
The authors would like to acknowledge Geoffrey Vun Yong An, Jonathan Wong Teck Kee and Mohd. Azizan Bin Ghani
for their time in collecting the required data at the Core. The assistance of Hjh. Saidah Hj. Mirasan, senior meteorologicalobserver, Brunei Darussalam Meteorological Services (BDMS) is highly appreciated for her positive cooperation in
obtaining the weather data. Also, the authors would like to thank all the anonymous reviewers and Professor K. McIsaac
(University of Western Ontario, Canada) for their valuable suggestions in improving the quality of this paper.
References
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López, G., F.J. Batlles, and J. Tovar-Pescador. 2005. “Selection of Input Parameters to Model Direct Solar Irradiance by
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Rivington, M., G. Bellocchi, K. B. Matthews, and K. Buchan. 2005. “Evaluation of Three Model Estimations of Solar
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International Journal of Sustainable
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Publicat ion det ails, including inst ruct ions f or aut hors and
subscript ion inf ormat ion:
ht t p: / / www. t andf online. com/ loi/ gsol20
Investigation of the effect of weather
conditions on solar radiation in Brunei
Darussalam
a
a
M. G. Yazdani , M. A. Salam & Q. M. Rahman
b
a
Facult y of Engineering, Inst it ut Teknologi Brunei, Bandar Seri
Begawan, Brunei Darussalam
b
Depart ment of Elect rical and Comput er Engineering, Universit y
of West ern Ont ario, London, ON, Canada N6A 5B9
Published online: 13 Oct 2014.
To cite this article: M. G. Yazdani, M. A. Salam & Q. M. Rahman (2014): Invest igat ion of t he ef f ect
of weat her condit ions on solar radiat ion in Brunei Darussalam, Int ernat ional Journal of Sust ainable
Energy, DOI: 10. 1080/ 14786451. 2014. 969266
To link to this article: ht t p: / / dx. doi. org/ 10. 1080/ 14786451. 2014. 969266
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International Journal of Sustainable Energy, 2014
http://dx.doi.org/10.1080/14786451.2014.969266
Investigation of the effect of weather conditions on solar
radiation in Brunei Darussalam
M.G. Yazdania , M.A. Salama and Q.M. Rahmanb∗
a Faculty of Engineering, Institut Teknologi Brunei, Bandar Seri Begawan, Brunei Darussalam;
b Department of Electrical and Computer Engineering, University of Western Ontario, London, ON,
Canada N6A 5B9.
(Received 20 June 2014; accepted 16 September 2014)
The amount of solar radiation received on the earth’s surface is known to be highly influenced by the
weather conditions and the geography of a particular area. This paper presents some results of an investigation that was carried out to find the effects of weather patterns on the solar radiation in Brunei
Darussalam, a small country that experiences equatorial climate due to its geographical location. Weather
data were collected at a suitable location in the University Brunei Darussalam (UBD) and were compared with the available data provided by the Brunei Darussalam Meteorological Services (BDMS). It
has been found that the solar radiation is directly proportional to the atmospheric temperature while it is
inversely proportional to the relative humidity. It has also been found that wind speed has little influence
on solar radiation. Functional relationships between the solar radiation and the atmospheric temperature,
and between the solar radiation and the relative humidity have also been developed from the BDMS
weather data. Finally, an artificial neural network (ANN) model has been developed for training and
testing the solar radiation data with the inputs of temperature and relative humidity, and a coefficient of
determination of around 99% was achieved. This set of data containing all the aforementioned results may
serve as a guideline on the solar radiation pattern in the geographical areas around the equator.
Keywords: solar energy; temperature; relative humidity; wind speed; ANN; correlation
1.
Introduction
It is crucial to determine the patterns of solar radiation throughout the year as solar technologies
heavily rely on these patterns. Both weather conditions and geography of a location have an
influence on solar radiation pattern as demonstrated in many research findings (Coops, Waring,
and Moncrieff 2000; Rivington et al. 2005; Yorukoglu and Celik 2006; FumitoshiNomiyama,
Murakami, and Murata 2011; Shi et al. 2011; Hill et al. 2012). Yorukoglu and Celik (2006) have
presented a review of the estimation of daily global solar radiation from sunshine duration and
demonstrated the interdependency between solar radiation and weather conditions by studying
the meteorological data.
A detailed evaluation of the performance and characteristic behaviour of two air temperaturebased models and one sunshine duration conversion method of estimating solar radiation for
24 meteorological stations has been carried out in Britain (Rivington et al. 2005). Comparisons
*Corresponding author. Email: qrahman@eng.uwo.ca
© 2014 Taylor & Francis
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2
M.G. Yazdani et al.
between these methods were made using a fuzzy logic-based multiple-indices assessment system
(Irad) and tests of the temporal distribution of mean errors over a year. It has been demonstrated that the two air temperatures-based method can be a reliable alternative when only air
temperature data are available.
Coops, Waring, and Moncrieff (2000) have developed a set of general equations for estimating
diffuse and direct solar radiation on the basis of mean daily maximum and minimum temperatures, latitude, elevation, slope and aspect. These proposed equations have been tested under
different climatic conditions and the resultant variation of solar radiation based on these different climatic conditions has been demonstrated. In another research work (FumitoshiNomiyama,
Murakami, and Murata 2011), a set of three methods to foresee global solar radiation using
weather forecast has been proposed and verified. An algorithm has been proposed by Shi and
others (Shi et al. 2011) to predict the output of a photovoltaic system based on weather data. For
modelling the solar irradiance, a multilayer feed forward artificial neural network (ANN) has
been used by a group of researchers (López, Batlles, and Tovar-Pescador 2005) with selected
input weather parameters. Furthermore, Assi et al. (2013) presented a Multi-layer PerceptronANN model for predicting global solar radiation (GSR) for some major cities in the United
Arab Emirates (UAE); the authors used the weather data between 1995 and 2006 to train the
neural network, while the data from the year 2007 were used to validate the model. Yao et al.
(2014) have evaluated some existing solar radiation models to estimate the solar radiation in
different weather conditions in Shanghai, China, and then established Monthly Average Daily
Global Solar Radiation (MADGSR) models and Daily Global Solar Radiation (DGSR) models
according to 42 years’ local weather data.
All these above-mentioned studies took into account the weather information and demonstrated that the variations in weather conditions decidedly influence the solar radiation pattern.
Bearing this idea in mind, the authors in this paper present some investigative results on the effect
of temperature, relative humidity and wind speed over the solar radiation pattern and develop a
functional relationship among them. Moreover, due to the fact that ANN techniques predict solar
radiation more accurately in comparison with conventional methods (Yadav and Chandel 2014),
the authors have developed an ANN model using back the propagation algorithm to evaluate the
solar radiation based on some selected weather inputs. The location for the experiments was set in
Brunei Darussalam (Geography and Map of Brunei), a small country that experiences a tropical
rainforest climate of hot and humid weather with heavy rainfall throughout the year. Based on the
weather condition of this country, experimental measurements on weather components have been
carried out for this paper. A discussion on the results obtained for different weather items has
been presented here. Also, the empirical relationship between the solar radiation and the weather
data (temperature and relative humidity) has been verified using the neural network model.
2.
Experimental measurement
Solar energy is generally determined by the radiation received from the sun over a wavelength
ranging from 300 to 4750 nm. Although it is essential to determine the solar radiation pattern
throughout the year, in this study, the experiment was carried out during the months of January
and February only. Since Brunei Darussalam is located near the equator, and the average weather
conditions do not vary much throughout the year, the choice of experimental time frame was
reasonably justified. The experiment was carried out during the midday period because maximum
solar radiation is generally received during that time of the day from a clear sky.
Weather data with mean temperature, relative humidity and air velocity were recorded using a
®
®
calibrated Kestrel 3000 Pocket Weather Meter. This metre has a maximum relative expanded
uncertainty for wind speed, temperature and relative humidity of ±0.60%, ±0.020◦ C and
International Journal of Sustainable Energy
Table 1.
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Date
Measured data for January 2013, from 1.00 pm to 2.00 pm.
Temperature (°C)
Relative humidity (%)
Avg. wind Speed (m/s)
33.1
32.0
31.1
31.4
30.6
29.4
31.7
31.8
29.6
77
68
70
73
72
75
74
75
81
0.8
1.2
1.0
0.9
1.3
1.2
1.4
1.1
0.8
21
22
23
24
26
28
29
30
31
Table 2.
Date
3
Measured data for February 2013, from 1.00 pm to 2.00 pm.
Temperature (°C)
Relative humidity (%)
Avg. Wind Speed (m/s)
31.8
31.4
30.8
30.9
29.4
31.7
31.4
32.1
31.2
30.3
30.7
29.3
32.1
29.2
33.1
32.6
26.4
76
73
73
74
87
75
72
69
70
76
75
73
71
79
63
71
96
0.9
1.2
1.1
1.3
1.2
1.1
1.4
1.2
0.7
0.8
1.0
1.8
1.6
2.4
0.8
1.1
1.7
2
4
5
6
7
9
13
14
16
18
19
20
21
25
26
27
28
±0.50% (within the wind speed range between 3 and 40 m/s), respectively. These three parameters were logged at a suitable location known as the Core in the University Brunei Darussalam
(UBD) on selected days between 21 January and 28 February 2013, during a midday time period
between 1:00 pm and 2:00 pm. The above data items were then collected and compared with
the available data obtained from the BDMS. The data provided by the BDMS were measured in
two different districts of Brunei Darussalam: Kuala Belait (KB) and Brunei International Airport
(BIA). The recorded average data obtained in the Core are shown in Tables 1 and 2, respectively.
®
®
Using a Kestrel 3000 Pocket Weather Meter, each of the data items had been averaged over ten
readings before it was recorded on the table.
3.
Results and discussion
This section provides a complete discussion on the recorded weather data during the months of
January and February. The weather data items include average temperature, relative humidity,
wind speed and the resultant solar radiation.
3.1.
Average temperature
As shown in Table 1, it was observed that the average temperature would fall in the range of a
minimum of 29.4°C to a maximum of 33.1°C with a mean of 31.0°C for the month of January.
4
M.G. Yazdani et al.
34
Average tepmerature (deg. C)
BIA
KB
Core
32
31
30
29
28
27
26
25
21
Figure 1.
22
23
24
25
26
Date
27
28
29
30
31
25
28
Average temperatures for January 2013, from 1:00 pm to 2:00 pm.
36.0
34.0
Average temperature (deg. C)
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33
32.0
30.0
28.0
26.0
BIA
24.0
KB
22.0
Core
20.0
1
Figure 2.
4
7
10
13
16
Date
19
22
Average temperatures for February 2013, from 1:00 pm to 2:00 pm.
High relative humidity was experienced throughout this month. During this time of the year,
the lowest, mean and highest average recorded relative humidity values were 68%, 74% and
81%, respectively. The highest humidity was observed during the rainy days. At the same time
in January, the highest, mean and lowest wind speeds of 1.4, 1.1 and 0.8 m/s, respectively, were
recorded. For the month of February, the recorded highest temperature was still at 33.1°C, but the
lowest was 26.4°C. Due to higher rainfall in this month, higher relative humidity was observed
with highest and lowest readings of 94% and 69%, respectively. The recorded wind speed for this
month ranged from a minimum of 0.7 m/s to a maximum of 2.4 m/s. The average temperatures
in January and February for the three places (BIA, Core and KB) are shown in Figures 1 and 2,
respectively. From Figures 1 and 2, it is observed that the measured data at the Core are higher
than that of KB and BIA. Also, from these two figures, it is seen that the average temperature is
generally warmer in January compared with the average temperature in February.
International Journal of Sustainable Energy
5
28.5
Temperature (deg. C)
27.5
2011
2012
27
26.5
1
2
3
4
5
6
7
8
9
10
11
12
30
31
Months
Figure 3.
Monthly average temperature readings.
90
BIA
85
Agerage relative humidity (%)
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28
KB
Core
80
75
70
65
60
21
22
23
24
25
26
27
28
29
Date
Figure 4.
Average relative humidity for January 2013, from 1:00 pm to 2:00 pm.
Figure 3 plots data for the monthly average temperature for BIA. It can be seen from Figure 3
that the annual average temperature in 2012 was higher than that of the readings for 2011,
which were calculated to be 27.8°C (2012) and 27.6°C (2011), respectively. It is interesting
to observe that the data in Figure 3 follow National Aeronautics and Space Administration’s
(NASA) prediction in their long-term climate warming trend.
3.2.
Relative humidly
The average relative humidity was found to be fluctuating significantly in all three places in the
month of January. The fluctuation was not as significant in the month of February, especially,
during mid-February between the dates 11 and 20 as shown in Figures 4 and 5. It is observed
that the relative humidity in KB is much lower than the other two places during the month of
February. The maximum relative humidity experienced in BIA was recorded at 94%, while the
6
M.G. Yazdani et al.
100
Average relative humidity (%)
80
70
60
50
BIA
40
KB
Core
30
1
4
7
10
13
16
19
22
25
28
Date
Figure 5.
Average relative humidity for February 2013, from 1:00 pm to 2:00 pm.
86
2011
Average relative humidity (%)
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90
84
2012
82
80
78
76
1
2
3
4
5
6
7
8
9
10
11
12
Months
Figure 6.
Monthly average relative humidity.
minimum was recorded at 56%. In KB, those corresponding values were 79.8% and 57.8%,
respectively. The mean relative humidity was found to be at 70.8% in BIA and at 70.4% in
KB for the two months. The monthly average relative humidity percentages for 2011 and 2012
are shown in Figure 6 for BIA. From Figure 6, for both 2011 and 2012, a decreasing trend in
relative humidity is observed from January to May. However, there is a fluctuation during the
mid-year and a rising trend starting from October. The average relative humidity for 2012 was
lower compared with 2011 which were 79.7% and 80.9%, respectively.
3.3.
Wind speed
The average wind speeds for the months of January and February are shown in Figures 7 and 8,
respectively. The average wind speed in January for BIA site was higher compared with the other
International Journal of Sustainable Energy
7
6
Average wind speed (m/s)
BIA
KB
4
Core
3
2
1
0
21
Figure 7.
22
23
24
25
26
Date
27
28
29
30
31
Average wind speeds for January 2013, from 1:00 pm to 2:00 pm.
5
BIA
Average wind speed (m/s)
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5
4
KB
Core
3
2
1
0
1
Figure 8.
4
7
10
13
16
Date
19
22
25
28
Average wind speeds for February 2013, from 1:00 pm to 2:00 pm.
sites. The maximum wind speed was found to be 5 m/s in January as shown in Figure 7. The
minimum was observed around 1 m/s in both January and February as can be seen in Figures 7
and 8. The monthly average wind speeds in BIA for the year 2011 and 2012 are shown in
Figure 9. The maximum and minimum wind speeds for the two years were found to be 5.8 and
4 m/s, respectively. It was also observed that the maximum, minimum and mean variations of
wind speed were 0.9, 0.5 and 0.7 m/s, respectively.
3.4.
Solar radiation
The average solar radiations recorded by the BDMS in KB are shown in Figure 10. The trends
of solar radiation for the year 2011 and 2012 are similar. However, most of the monthly radiation
values for the year 2012 are found to be higher compared with the corresponding values for the
year 2011. The above observation is also supported by the NASA’s findings in the year 2013
8
M.G. Yazdani et al.
7
2011
Average wind speed (m/s)
2012
5
4
3
2
1
0
1
2
3
4
5
6
7
8
9
10
11
12
Months
Figure 9.
Monthly average wind speeds.
6
2011
2012
Average radiation (kW/m2)
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6
5.5
5
4.5
4
1
2
3
4
5
6
7
8
9
10
11
12
Months
Figure 10. Monthly average solar radiation.
from the Goddard Institute for Space Studies (GISS) which stated that, globally, 2012 was the
ninth warmest year.
3.5.
Overall discussion
Collected data at the Core were compared with the data from BIA and KB to investigate their
patterns, similarities and differences. The Core data readings were found to be generally higher
than the data obtained from the BDMS. Since data were collected using a handheld device at
the Core while the data from BIA and KB were collected from the weather stations, the latter is
thought to be more accurate. From Figures 1 and 2, it is observed that all the data items show
a similar trend. Since the solar radiation data were available only from KB, the analysis on the
variation of solar radiation was done using the data from KB.
International Journal of Sustainable Energy
Development of correlation
In general, there is an influence of weather condition on the solar radiation (SR) received on
the earth’s surface. The radiation data obtained from KB are plotted against the temperature
and relative humidity to find the correlations between the solar radiation and the atmospheric
temperature, and between the solar radiation and the relative humidity. From the recorded data
provided by the BDMS, it was observed that the wind speed had little or no influence over the
solar radiation as can be seen in Figures 11 and 12. Therefore, the wind speed was not considered
as a parameter in developing the correlation.
To find the relationship between solar radiations with other two components, solar radiation
data were initially plotted over average temperature and relative humidity as shown in Figures
13 and 14, respectively. Later on, a best-fit polynomial curve was used to get the correlation
between the solar radiation and the atmospheric temperature, and between the solar radiation
5.6
5.4
5.2
SR (kW/m2)
5
4.8
4.6
4.4
4.2
4
0.2
2.2
4.2
6.2
8.2
Wind speed (m/s)
Figure 11.
Avg. solar radiation versus wind speed in 2011.
6
5.5
SR (kW/m2)
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4.
9
5
4.5
4
3.5
3
0.5
0.6
0.7
0.8
Wind speed (m/s)
Figure 12.
Avg. solar radiation versus wind speed in 2012.
0.9
1
Avg. solar radiation (kW/m2)
M.G. Yazdani et al.
6.0
5.8
5.6
5.4
5.2
5.0
4.8
4.6
4.4
4.2
4.0
26.5
y = -0.0042x3 + 0.2443x2 - 3.33x
R² = 0.3285
27.0
27.5
28.0
28.5
Avg. temperature (deg. C)
Figure 13. Avg. solar radiation versus avg. temperature.
6.0
Avg. solar radiation(kW/m2)
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10
5.8
5.6
5.4
5.2
5.0
4.8
4.6
y = -0.0002x3 + 0.0306x2 - 1.0862x
R² = 0.4575
4.4
4.2
4.0
76
77
78
79
80
81
82
83
84
85
Avg. relative humidity (%)
Figure 14. Avg. solar radiation versus avg. relative humidity.
and the relative humidity. These correlations are given as follows:
SR = −0.00042t3 + 0.2443t2 − 3.3t
3
2
SR = −0.0002RH + 0.0306RH − 1.0862RH
(1)
(2)
The best-fit polynomial curve in Figure 13 shows that the solar radiation appears to be directly
proportional to atmospheric temperature, while in Figure 14, with the aid of the best-fit polynomial curve, it is observed that the solar radiation is inversely proportional to relative humidity.
In a similar way, an attempt was made to correlate both the temperature and relative humidity
with the solar radiation by plotting t/RH versus SR data items as shown in Figure 15. The data
generally collapsed into a trend line, as shown by the best-fit power curve in Figure 15. This
trend line can be expressed as
t 0.306
(3)
SR = 7.07
RH
4.1.
ANN model
The ANN model has been developed using back propagation algorithm to find the solar radiation
based on the selected inputs from the raw data. In this model, three layers with two hidden
International Journal of Sustainable Energy
11
6
solar radiation (W/m2)
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y = 7.0701x 0.3065
R² = 0.1066
5.5
5
4.5
4
0.32
0.33
0.34
0.35
0.36
0.37
t/RH
Figure 15.
Variation of solar radiation versus t/RH.
Inputs
Temp.
w11
Hidden layers
w12
Output
SR
w21
RH
w22
Figure 16.
Generalised ANN model.
6
5
4
Actual
3
Predicted
2
1
0
1
Figure 17.
3
Training results for the year 2011.
5
7
9
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12
M.G. Yazdani et al.
neurons and a single output have been considered. Here, the temperature and relative humidity
have been considered as the input parameters and the solar radiation has been considered as the
output parameter. As shown in Figure 16, the Group Method for Data Handling (GMDH) ANN
software has been used for modelling. This ANN model has been used to train and test the solar
radiation pattern for the years 2011 and 2012. During the training phase, eight data sets have
been used, while, four data sets have been used during the testing phase. The predicted output
from each of the training sets has been recorded and plotted as shown in Figures 17 and 18, for
the years 2011 and 2012, respectively. The training processes have converged to the threshold
values of 0.078667 (Year: 2011) and 0.069998 (Year: 2012), using two hidden layers.
For modelling, the GMDH neuron function has been considered as
f (x) = a0 + a1 x1 + a2 x2 + a3 x1 x2 + a4 x1 2 + a5 x2 2
(4)
Figures 17 and 18 demonstrate that the actual and the predicted data items are in good agreement during the training session. The coefficients of determination (R2 ) for the training models
have been found to be 0.997925 and 0.997926 for the years 2011 and 2012, respectively. These
6
5
4
Atual
Predicted
3
2
1
0
1
2
3
4
Figure 18. Training results for the year 2012.
7
6
5
Actual
4
Predicted
3
2
1
0
1
3
Figure 19. Testing results for the year 2011.
5
7
International Journal of Sustainable Energy
13
7
6
5
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4
Actual
3
Predicted
2
1
0
1
Figure 20.
2
3
4
Testing results for the year 2012.
high percentages of coefficients of determination indicate that the variation in solar radiation
could be obtained by the selected two weather-input variables.
After training the model, the next phase was to validate the model for its accuracy. In this case,
four data sets have been used for testing the models. The test results were found to be in good
agreement with the actual results, and in this case, the coefficients of determination were found
to be 0.977456 (Year 2011) and 0.984924 (Year: 2012). The test phase data have been plotted in
Figures 19 and 20.
5.
Conclusion
The relative humidity, average temperature and average wind speed have been measured at the
Core site of the UBD and are compared with the published data provided by the BDSM. The
functional relationships between the solar radiation and both the temperature and relative humidity have been proposed using a best-fit polynomial curve. It is also found that the wind speed has
almost no influence on the solar radiation. In addition, a highly accurate ANN model for predicting the solar radiation pattern, based on two inputs, has been developed which has provided
close to 100% coefficients of determination (0.997925 and 0.977456, 0.997926 and 0.984924)
for both training and testing phases for the 2011 and 2012 year data. This model can be used for
future prediction of solar radiation pattern with the selected weather-input variables.
Acknowledgements
The authors would like to acknowledge Geoffrey Vun Yong An, Jonathan Wong Teck Kee and Mohd. Azizan Bin Ghani
for their time in collecting the required data at the Core. The assistance of Hjh. Saidah Hj. Mirasan, senior meteorologicalobserver, Brunei Darussalam Meteorological Services (BDMS) is highly appreciated for her positive cooperation in
obtaining the weather data. Also, the authors would like to thank all the anonymous reviewers and Professor K. McIsaac
(University of Western Ontario, Canada) for their valuable suggestions in improving the quality of this paper.
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