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Comparative study in predicting the global solar radiation for Darwin, Australia / Wai Kean Yap in Transactions of the ASME. Journal of solar energy engineering, Vol. 134 N° 3 (Août 2012) 

Public ISBD [article]  in Transactions of the ASME. Journal of solar energy engineering > Vol. 134 N° 3 (Août 2012) . - 06 p. Titre : Comparative study in predicting the global solar radiation for Darwin, Australia Type de document : texte imprimé Auteurs : Wai Kean Yap, Auteur ; Vishy Karri, Auteur Année de publication : 2012 Article en page(s) : 06 p. Note générale : solar energy Langues : Anglais (eng) Mots-clés : solar  radiation  prediction;  linear  regression  analysis;  angstrom-prescott-page;  artificial  neural  network Index. décimale : 621.47 Résumé : 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. DEWEY : 621.47 ISSN : 0199-6231 En ligne : http://asmedl.org/getabs/servlet/GetabsServlet?prog=normal&id=JSEEDO000134000003 [...] [article] Comparative study in predicting the global solar radiation for Darwin, Australia [texte imprimé] / Wai Kean Yap, Auteur ; Vishy Karri, Auteur . - 2012 . - 06 p. solar energy Langues : Anglais (eng) in Transactions of the ASME. Journal of solar energy engineering > Vol. 134 N° 3 (Août 2012) . - 06 p. Mots-clés : solar  radiation  prediction;  linear  regression  analysis;  angstrom-prescott-page;  artificial  neural  network Index. décimale : 621.47 Résumé : 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. DEWEY : 621.47 ISSN : 0199-6231 En ligne : http://asmedl.org/getabs/servlet/GetabsServlet?prog=normal&id=JSEEDO000134000003 [...]