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On: 12 Novem ber 2014, At : 19: 42 Publisher : Taylor & Francis

I nfor m a Lt d Regist er ed in England and Wales Regist er ed Num ber : 1072954 Regist er ed office: Mor t im er House, 37- 41 Mor t im er St r eet , London W1T 3JH, UK

International Journal of Sustainable

Energy

Publ icat ion det ail s, incl uding inst ruct ions f or aut hors and subscript ion inf ormat ion:

ht t p: / / www. t andf onl ine. com/ l oi/ gsol 20

Investigation of the effect of weather

conditions on solar radiation in Brunei

Darussalam

M. G. Yazdania, M. A. Sal ama & Q. M. Rahmanb

a

Facul t y of Engineering, Inst it ut Teknol ogi Brunei, Bandar Seri Begawan, Brunei Darussal am

b

Depart ment of El ect rical and Comput er Engineering, Universit y of West ern Ont ario, London, ON, Canada N6A 5B9

Publ ished onl ine: 13 Oct 2014.

To cite this article: M. G. Yazdani, M. A. Sal am & Q. M. Rahman (2014): Invest igat ion of t he ef f ect of weat her condit ions on sol ar radiat ion in Brunei Darussal am, Int ernat ional Journal of Sust ainabl e 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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and- condit ions

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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. Salamaand Q.M. Rahmanb∗

aFaculty of Engineering, Institut Teknologi Brunei, Bandar Seri Begawan, Brunei Darussalam; bDepartment 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 inves-tigation 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 com-pared 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 Moncrieff2000; Rivington et al.2005; Yorukoglu and Celik2006; FumitoshiNomiyama, Murakami, and Murata2011; 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 temperature-based 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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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 demon-strated 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 tempera-tures, 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 differ-ent 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-Pescador2005) with selected input weather parameters. Furthermore, Assi et al. (2013) presented a Multi-layer Perceptron-ANN 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 demon-strated 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 Chandel2014), 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

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Table 1. Measured data for January 2013, from 1.00 pm to 2.00 pm.

Date Temperature (°C) Relative humidity (%) Avg. wind Speed (m/s)

21 33.1 77 0.8

Table 2. Measured data for February 2013, from 1.00 pm to 2.00 pm.

Date Temperature (°C) Relative humidity (%) Avg. Wind Speed (m/s)

2 31.8 76 0.9

±0.50% (within the wind speed range between 3 and 40 m/s), respectively. These three param-eters 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 Tables1and2, 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 Table1, 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.

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25

Figure 1. Average temperatures for January 2013, from 1:00 pm to 2:00 pm.

20.0

Figure 2. 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 Figures1and2, respectively. From Figures1and2, 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.

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26.5 27 27.5 28 28.5

1 2 3 4 5 6 7 8 9 10 11 12

Tem

p

erature (deg.

C)

Months

2011

2012

Figure 3. Monthly average temperature readings.

60 65 70 75 80 85 90

21 22 23 24 25 26 27 28 29 30 31

Agerage relative hum

idity

(%)

Date

BIA

KB

Core

Figure 4. Average relative humidity for January 2013, from 1:00 pm to 2:00 pm.

Figure3plots data for the monthly average temperature for BIA. It can be seen from Figure3 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 Figure3 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 Figures4and 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

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30 40 50 60 70 80 90 100

1 4 7 10 13 16 19 22 25 28

Average relative hum

idity

(%)

Date

BIA KB

Core

Figure 5. Average relative humidity for February 2013, from 1:00 pm to 2:00 pm.

76 78 80 82 84 86

1 2 3 4 5 6 7 8 9 10 11 12

Average relative hum

idity

(%)

Months

2011

2012

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 6for BIA. From Figure6, 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 Figures7and 8, respectively. The average wind speed in January for BIA site was higher compared with the other

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0

Figure 7. Average wind speeds for January 2013, from 1:00 pm to 2:00 pm.

0

Figure 8. 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 Figure10. 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

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0

Figure 9. Monthly average wind speeds.

4

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 Figures1and2, 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.

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4. 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 Figures11and12. 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 13and14, 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

4 4.2 4.4 4.6 4.8 5 5.2 5.4 5.6

0.2 2.2 4.2 6.2 8.2

SR (kW

/m

2)

Wind speed (m/s)

Figure 11. Avg. solar radiation versus wind speed in 2011.

3 3.5 4 4.5 5 5.5 6

0.5 0.6 0.7 0.8 0.9 1

SR (k

W

/m

2)

Wind speed (m/s)

Figure 12. Avg. solar radiation versus wind speed in 2012.

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y = -0.0042x3 + 0.2443x2 - 3.33x

26.5 27.0 27.5 28.0 28.5

Av

g. sol

ar

radiation (kW/m

2)

Avg. temperature (deg. C)

Figure 13. Avg. solar radiation versus avg. temperature.

y = -0.0002x

3

+ 0.0306x

2

- 1.0862x

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 (1)

SR= −0.0002RH3+0.0306RH2−1.0862RH (2)

The best-fit polynomial curve in Figure13shows that the solar radiation appears to be directly proportional to atmospheric temperature, while in Figure14, with the aid of the best-fit polyno-mial 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 Figure15. 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

SR=7.07

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

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y = 7.0701x0.3065 R² = 0.1066

4 4.5 5 5.5 6

0.32 0.33 0.34 0.35 0.36 0.37

solar rad

iation (W

/m

2)

t/RH

Figure 15. Variation of solar radiation versus t/RH.

w11

w12

w21

w22

Inputs

Hidden layers

Output Temp.

RH

SR

Figure 16. Generalised ANN model.

0 1 2 3 4 5 6

1 3 5 7 9

Actual

Predicted

Figure 17. Training results for the year 2011.

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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 Figure16, 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 Figures17and18, 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+a1x1+a2x2+a3x1x2+a4x12+a5x22 (4)

Figures17and18demonstrate that the actual and the predicted data items are in good agree-ment 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

0 1 2 3 4 5 6

1 2 3 4

Atual

Predicted

Figure 18. Training results for the year 2012.

0 1 2 3 4 5 6 7

1 3 5 7

Actual

Predicted

Figure 19. Testing results for the year 2011.

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0 1 2 3 4 5 6 7

1 2 3 4

Actual

Predicted

Figure 20. 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 Figures19and20.

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 humid-ity 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 pre-dicting 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 meteorological-observer, 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

Assi, A. H., M. H. Al-Shamisi, H. A. N. Hejase, and A. Haddad. 2013. “Prediction of Global Solar Radiation in UAE using Artificial Neural Networks.” IEEE International Conference on Renewable Energy Research and Applications, Madrid, Spain, October 20–23, pp. 196–200.

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Coops, N. C., R. H. Waring, and J. B. Moncrieff. 2000. “Estimating Mean Monthly Incident Solar Radiation on Hori-zontal and Inclined Slopes from Mean Monthly Temperatures Extremes.” International Journal of Biometeorology 44 (4): 204–211.

FumitoshiNomiyama, JojiAsai, Takuma Murakami, and Junichi Murata. 2011. “A Study on Global Solar Radiation Fore-casting Using Weather Forecast Data.” IEEE 54thInternational Symposium Midwest on Circuits and Systems, pp.

1–4.

“Geography and Map of Brunei.” Brunei Darussalam, 23 April 2013.http://www.theodora.com/wfbcurrent/brunei/ index.html

Hill, Cody A., Matthew Clayton Such, Dongmei Chen, Juan Gonzalez, and W. Mack Grady. 2012, June. “Battery Energy Storage for Enabling Integration of Distributed Solar Power Generation.” IEEE Transactions on Smart Grid 3 (2): 850–857.

López, G., F.J. Batlles, and J. Tovar-Pescador. 2005. “Selection of Input Parameters to Model Direct Solar Irradiance by using Artificial Neural Networks.” Journal of Energy 30 (9): 1675–1684.

Rivington, M., G. Bellocchi, K. B. Matthews, and K. Buchan. 2005. “Evaluation of Three Model Estimations of Solar Radiation at 24 UK Stations.” Journal of Agriculture and Forest Meteorology 132 (3–4): 228–243.

Shi, Jie, Wei-Jen Lee, Yongqian Liu, Yongping Yang, and Peng Wang. 2011. “Forecasting Power Output of Photovoltaic System Based on Weather Classification and Support Vector Machine.” IEEE Industry Society Annual Meeting, Orlando, FL, USA, October 9–13, pp. 1–6.

Yadav, Amit Kumar, and S. S. Chandel. 2014, May. “Solar Radiation Prediction Using Artificial Neural Network Techniques: A Review.” Journal of Renewable and Sustainable Energy Reviews 33 (5): 772–781.

Yao, Wanxiang, Zhengrong Li, Yuyan Wang, Fujian Jiang, and Lingzhou Hu. 2014. “Evaluation of Global Solar Radiation Models for Shanghai, China.” Journal of Energy Conversion and Management 84 (4): 597–612. Yorukoglu, M., and A. N. Celik. 2006. “A Critical Review on the Estimation of Daily Global Solar Radiation from

Sunshine Duration.” Energy Conversion & Management 132 (15–16): 2441–2450.

Gambar

Table 1.Measured data for January 2013, from 1.00 pm to 2.00 pm.
Figure 1.Average temperatures for January 2013, from 1:00 pm to 2:00 pm.
Figure 3.Monthly average temperature readings.
Figure 5.Average relative humidity for February 2013, from 1:00 pm to 2:00 pm.
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