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A new approach for meteorological variables prediction at Kuala Lumpur, Malaysia, using artificial neural networks / Tamer Khatib in Transactions of the ASME. Journal of solar energy engineering, Vol. 134 N° 2 (Mai 2012) 

Public ISBD [article]  in Transactions of the ASME. Journal of solar energy engineering > Vol. 134 N° 2 (Mai 2012) . - 10 p. Titre : A new approach for meteorological variables prediction at Kuala Lumpur, Malaysia, using artificial neural networks : application for sizing and maintaining photovoltaic systems Type de document : texte imprimé Auteurs : Tamer Khatib, Auteur ; Azah Mohamed, Auteur ; M. Mahmoud, Auteur Année de publication : 2012 Article en page(s) : 10 p. Note générale : solar energy Langues : Anglais (eng) Mots-clés : meteorological  variables  prediction;  solar  energy;  ambient  temperature;  wind  speed;  relative  humidity;  PV  systems;  Malaysia Index. décimale : 621.47 Résumé : This research presents a new meteorological variables prediction approach for Malaysia using artificial neural networks. The developed model predicts four meteorological variables using sun shine ratio, day number, and location coordinates. These meteorological variables are solar energy, ambient temperature, wind speed, and relative humidity. However, three statistical values are used to evaluate the proposed model. These statistical values are mean absolute percentage error (MAPE), mean bias error (MBE), and root mean square error (RMSE). Based on results, the developed model predicts accurately the four meteorological variables. The MAPE, RMSE, and MBE in predicting solar radiation are 1.3%, 5.8 (1.8%), and 0.9 (0.3%), respectively, while the MAPE, RMSE, and MBE values for ambient temperature prediction are 1.3%, 0.4 (1.7%), and 0.1 (0.4%), respectively. In addition, the MAPE, RMSE, and MBE values in relative humidity prediction are 3.2%, 3.2, and 0.2. As for wind speed prediction, it is the worst in accuracy among the predicted variables because the MAPE, RMSE, and MBE values are 28.9%, 0.5 (31.3%), and 0.02 (1.25%). Such a developed model helps in sizing photovoltaic (PV) systems using solar energy and ambient temperature records. Moreover, wind speed and relative humidity records could be used in estimating dust concentration group which leads to dust deposition on a PV system. DEWEY : 621.47 ISSN : 0199-6231 En ligne : http://asmedl.org/getabs/servlet/GetabsServlet?prog=normal&id=JSEEDO000134000002 [...] [article] A new approach for meteorological variables prediction at Kuala Lumpur, Malaysia, using artificial neural networks : application for sizing and maintaining photovoltaic systems [texte imprimé] / Tamer Khatib, Auteur ; Azah Mohamed, Auteur ; M. Mahmoud, Auteur . - 2012 . - 10 p. solar energy Langues : Anglais (eng) in Transactions of the ASME. Journal of solar energy engineering > Vol. 134 N° 2 (Mai 2012) . - 10 p. Mots-clés : meteorological  variables  prediction;  solar  energy;  ambient  temperature;  wind  speed;  relative  humidity;  PV  systems;  Malaysia Index. décimale : 621.47 Résumé : This research presents a new meteorological variables prediction approach for Malaysia using artificial neural networks. The developed model predicts four meteorological variables using sun shine ratio, day number, and location coordinates. These meteorological variables are solar energy, ambient temperature, wind speed, and relative humidity. However, three statistical values are used to evaluate the proposed model. These statistical values are mean absolute percentage error (MAPE), mean bias error (MBE), and root mean square error (RMSE). Based on results, the developed model predicts accurately the four meteorological variables. The MAPE, RMSE, and MBE in predicting solar radiation are 1.3%, 5.8 (1.8%), and 0.9 (0.3%), respectively, while the MAPE, RMSE, and MBE values for ambient temperature prediction are 1.3%, 0.4 (1.7%), and 0.1 (0.4%), respectively. In addition, the MAPE, RMSE, and MBE values in relative humidity prediction are 3.2%, 3.2, and 0.2. As for wind speed prediction, it is the worst in accuracy among the predicted variables because the MAPE, RMSE, and MBE values are 28.9%, 0.5 (31.3%), and 0.02 (1.25%). Such a developed model helps in sizing photovoltaic (PV) systems using solar energy and ambient temperature records. Moreover, wind speed and relative humidity records could be used in estimating dust concentration group which leads to dust deposition on a PV system. DEWEY : 621.47 ISSN : 0199-6231 En ligne : http://asmedl.org/getabs/servlet/GetabsServlet?prog=normal&id=JSEEDO000134000002 [...]