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

Wai Kean Yap, Vishy Karri

    Research output: Contribution to journalArticleResearchpeer-review

    Abstract

    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.
    Original languageEnglish
    Article number034501
    Pages (from-to)1-6
    Number of pages6
    JournalJournal of Solar Energy Engineering, Transactions of the ASME
    Volume134
    Issue number3
    DOIs
    Publication statusPublished - 7 May 2012

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    Solar radiation
    Linear regression
    Neural networks
    Meteorology
    Mean square error
    Rain
    Evaporation
    Satellites
    Temperature

    Cite this

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    title = "Comparative Study in Predicting the Global Solar Radiation for Darwin, Australia",
    abstract = "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.",
    keywords = "Accurate prediction, Angstrom-Prescott- Page, Artificial neural network models, Australia, Climate data, Comparative studies, Data sets, Global solar radiation, LR method, Maximum temperature, Model complexity, Northern territories, Photovoltaic, Potential sites, Predictive models, PV installations, Radiation prediction, Root mean square errors, Solar exposure, Sunshine duration, Sunshine Hour, Climatology, Linear regression, Mean square error, Neural networks, Regression analysis, Solar radiation, Sun, Climate models",
    author = "Yap, {Wai Kean} and Vishy Karri",
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    doi = "10.1115/1.4006574",
    language = "English",
    volume = "134",
    pages = "1--6",
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    Comparative Study in Predicting the Global Solar Radiation for Darwin, Australia. / Yap, Wai Kean; Karri, Vishy.

    In: Journal of Solar Energy Engineering, Transactions of the ASME, Vol. 134, No. 3, 034501, 07.05.2012, p. 1-6.

    Research output: Contribution to journalArticleResearchpeer-review

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    T1 - Comparative Study in Predicting the Global Solar Radiation for Darwin, Australia

    AU - Yap, Wai Kean

    AU - Karri, Vishy

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    N2 - 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.

    AB - 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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    KW - Artificial neural network models

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    KW - Potential sites

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    KW - PV installations

    KW - Radiation prediction

    KW - Root mean square errors

    KW - Solar exposure

    KW - Sunshine duration

    KW - Sunshine Hour

    KW - Climatology

    KW - Linear regression

    KW - Mean square error

    KW - Neural networks

    KW - Regression analysis

    KW - Solar radiation

    KW - Sun

    KW - Climate models

    U2 - 10.1115/1.4006574

    DO - 10.1115/1.4006574

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