Harnessing solar power: Predicting photovoltaic potential in fiche, oromia, ethiopia with artificial neural networks

Abstract

In developing nations like Ethiopia, especially in rural and semi-urban regions, reliable access to electricity through grid-based systems is often limited or unavailable due to inadequate energy infrastructure. To tackle this issue, photovoltaic (PV) technology plays a crucial role in converting solar energy into electricity, especially in remote regions. This study focuses on assessing the daily and monthly energy exposure of PV arrays located near Fiche town, Ethiopia. The study utilizes four network types - Feedforward Backpropagation (FFBP), Cascade Feedforward Backpropagation (CFBP), Error Backpropagation (EBP), and Levenberg-Marquardt (LR) - with eight input variables and a training-testing split of 70%-30% to estimate global solar radiation (GSR). Hidden and output layers are employed with a sigmoid and linear function as activation functions, respectively. The results reveal that the expected mean daily and monthly horizontal global solar radiation (HGSR) ranges between 3.282 kWh/m2/day ≤ HGSR ≤ 6.967 kWh/m2/day and 4.628 kWh/m2 ≤ HGSR ≤ 6.613 kWh/m2, respectively. Furthermore, the study determines that the daily energy delivered to the PV system and available from the PV array to load falls within the range of 0.656 kWh/m2 ≤ Ed ≤ 1.394 kWh/m2 and 0.567 kWh/m2 ≤ El ≤ 1.205 kWh/m2, respectively. The average results for the study period from 2016 to 2021 were 1.175 kWh/m2 and 1.015 kWh/m2 for the Ed and El, respectively. These results demonstrate that PV arrays near Fiche town have abundant solar energy resources, making them highly attractive for electricity generation. Furthermore, utilizing solar energy from PV arrays can help mitigate indoor pollution in the rural communities surrounding Fiche town.

Keywords

• Energy access
• Network types
• Photovoltaic energy
• Solar radiation
• Rural electrification