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Technical Briefs

Comparative Study in Predicting the Global Solar Radiation for Darwin, Australia

[+] Author and Article Information
Wai Kean Yap1

Centre for Renewable Energy and Low Emissions Technology,  Charles Darwin University, Casuarina Campus, Ellengowan Drive Darwin, Northern Territory 0909, Australiawai.yap@cdu.edu.au

Vishy Karri

Australian College of Kuwait,P.O. Box 1411, Safat 13015, Kuwaitv.karri@ack.edu.kw

1

Corresponding author.

J. Sol. Energy Eng 134(3), 034501 (May 07, 2012) (6 pages) doi:10.1115/1.4006574 History: Received August 14, 2011; Revised August 14, 2011; Published May 07, 2012; Online May 07, 2012

This paper presents a comparative study in predicting the monthly average solar radiation for Darwin, Australia (latitude 12.46 deg S longitude 130.84 deg E). The city of Darwin, Northern Territory (NT), has the highest and most consistent sunshine duration among all the other Australian states. This unique climate presents an opportunity for photovoltaic (PV) applications. Reliable and accurate predictions of solar radiation enable potential site locations, which exhibit high solar radiations and sunshine hours, to be identified for PV installation. Three predictive models were investigated in this study—the linear regression (LR), Angstrom–Prescott–Page (APP), and the artificial neural network (ANN) models. The mean global solar radiation coupled with the climate data (mean minimum and maximum temperatures, mean rainfall, mean evaporation, and sunshine fraction) obtained from the Australian Bureau of Meteorology (BoM) formed the basis of the dataset. Using simple and easily obtainable climate data presents an added advantage by reducing model complexity. Predictive results showed the root mean square errors (RMSEs) obtained were 6.72%, 13.29%, and 8.11% for the LR, APP, and ANN models, respectively. The predicted solar exposure from the LR model was then compared with the satellite-derived data to assess the accuracy of the LR method.

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Copyright © 2012 by American Society of Mechanical Engineers
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References

Figures

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Figure 9

Comparison between the predicted and satellite solar radiation for 2010

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Figure 8

Comparison between the predicted and satellite solar radiation for 2009

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Figure 7

Comparison between the predicted and satellite solar radiation for 2008

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Figure 6

The measured and predicted values of solar radiation with ANN model

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Figure 5

Clearness index versus sunshine duration ratio

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Figure 4

The measured and calculated values of solar radiation with the linear regression model

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Figure 3

ANN architecture with five inputs and one output

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Figure 2

Annual solar radiation [29]

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Figure 1

Monthly average sunshine duration from 2000 to 2011 for Darwin

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