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dc.contributor.authorMubiru, J.
dc.contributor.authorBanda, E.J.K.B.
dc.date.accessioned2022-01-07T16:29:28Z
dc.date.available2022-01-07T16:29:28Z
dc.date.issued2008
dc.identifier.citationJ. 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.urihttps://nru.uncst.go.ug/xmlui/handle/123456789/1149
dc.description.abstractThis 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.language.isoenen_US
dc.publisherSolar Energyen_US
dc.subjectArtificial neural networks; Global solar irradiation; Sunshine hours; Cloud cover; Maximum temperature; Modelen_US
dc.titleEstimation Of Monthly Average Daily Global Solar Irradiation Using Artificial Neural Networksen_US
dc.typeArticleen_US


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