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

Abstract

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

Keywords

• CEBP
• EBP
• FFBP
• Fiche
• input variable
• LR