Estimation Of Monthly Average Daily Global Solar Irradiation Using Artificial Neural Networks

dc.contributor.author	Mubiru, J.
dc.contributor.author	Banda, E.J.K.B.
dc.date.accessioned	2022-01-07T16:29:28Z
dc.date.available	2022-01-07T16:29:28Z
dc.date.issued	2008
dc.description.abstract	This study explores the possibility of developing a prediction model using artificial neural networks (ANN), which could be used to estimate monthly average daily global solar irradiation on a horizontal surface for locations in Uganda based on weather station data: sunshine duration, maximum temperature, cloud cover and location parameters: latitude, longitude, altitude. Results have shown good agreement between the estimated and measured values of global solar irradiation. A correlation coefficient of 0.974 was obtained with mean bias error of 0.059 MJ/m2 and root mean square error of 0.385 MJ/m2. The comparison between the ANN and empirical method emphasized the superiority of the proposed ANN prediction model.	en_US
dc.identifier.citation	J. Mubiru, E.J.K.B. Banda, Estimation of monthly average daily global solar irradiation using artificial neural networks, Solar Energy,https://doi.org/10.1016/j.solener.2007.06.003.	en_US
dc.identifier.uri	https://nru.uncst.go.ug/xmlui/handle/123456789/1149
dc.language.iso	en	en_US
dc.publisher	Solar Energy	en_US
dc.subject	Artificial neural networks; Global solar irradiation; Sunshine hours; Cloud cover; Maximum temperature; Model	en_US
dc.title	Estimation Of Monthly Average Daily Global Solar Irradiation Using Artificial Neural Networks	en_US
dc.type	Article	en_US

Now showing 1 - 1 of 1

Name:: Estimation of monthly average daily global solar irradiation.pdf
Size:: 226.57 KB
Format:: Adobe Portable Document Format
Description:: Estimation of monthly average daily global solar irradiation using artificial neural networks

Now showing 1 - 1 of 1