Estimating solar radiation using artificial neural networks: a case study of Fiche, Oromia, Ethiopia

The precise assessment and evaluation of global solar radiation (GSR) is crucial for designing effective energy systems. However, in developing countries like Ethiopia, the cost maintenance measuring devices are inadequate. As a result, researchers have explored alternative methods such as empirical models to estimate GSR. This article proposes using artificial neural networks (ANN) predict daily monthly averaged horizontal GSR (HGSR) around Fiche town various network types. input variables were divided into training (70%) testing (30%) sets evaluate types, with sigmoid function used activation at hidden layer linear output layer. predicted mean HGSR ranges from 3.282 kWh/m2/day 6.967 4.628 kWh/m2 6.613 respectively. values obtained compared those provided by NASA observation data found be within acceptable limits. Statistical metrics MAPE, MSE, RMSE show that CFBP, FFBP, LR, EBP better types estimating HGSR, while EBP, LR HGSR. Overall, all ANN accurately In general, findings this study indicated location had promising producing electricity uses.