Solar Potential Assessment for Remote Electrification in Ethiopia: A Comparative Modelling Approach

Understanding the solar potential of remote regions is crucial for addressing energy poverty, promoting economic development, and mitigating the impacts of climate change, especially in underserved areas. However, access to comprehensive localized solar radiation data is severely limited in most remote regions due to lack of measuring devices and nonfunctional meteorological stations. In this research, we leverage spatially sparse dataset comprising records from 54 sites and meteorological data from 21 sites across Ethiopia, covering 13 years to predict the monthly global solar irradiance (GHI) for remote inaccessible areas where historical data is unavailable. Climatic variables, such as relative sunshine duration and temperatures are used for computing GHI using dual models. To estimate monthly GHI for regions lacking historical data, deterministic inverse distance weighting (IDW) and Geo statistic Kriging cross validation and interpolation techniques have been employed. The analysis results indicated that a solar potential varies from July (3.70 to 6.54) kWh/m<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>/day in March, based on Prescott model, while its value varied from 3.65 to 7.26 kWh/m<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>/day in similar months based on Allen model. Our findings revealed that IDW approaches provide a better spatial resolution compared to the Kriging technique. Based on the mean per months bases Statistical error model analysis exhibited accurate, reliable, and outstanding performance, showcasing minimal Root Mean Square Error (RMSE) values of 0.15, 0.21, and 0.05, along with Mean Absolute Percentage Error (MAPE) values of 0.29, 0.83, and 0.79 for the Kriging technique applied to sunshine, temperature and NASA respectively. This study's solar radiation predictions offer crucial insights for off-grid design, policy development, and sustainable rural electrification.