GIS-based Hotspot Assessment of Solar Energy Potentials: African Countries Clustering

Abstract

Abstract This study introduces an approach to quantify solar energy potential and identify hotspot areas; cluster map the solar energy high potential countries in Africa for sustainable development of solar PV technology-based power using GIS spatial analysis. The research integrated geographical GHI, and DNI data, and other geospatial information to evaluate the total solar potential in the African region. The results revealed that Africa, with a total land area of 3.41X107 km2, was found to have substantial potential as high of SEGHI at 7.41X1010 kWh, and SEDNI at 2.72X109 kWh. These results affirm Africa's solar energy potential as a valuable resource to address energy demands, foster economic growth, and reduce greenhouse gas emissions. Additionally, Moran’s I was employed to analyses autocorrelation in countries' solar energy resources, revealing spatial patterns. Wealthy solar energy clusters were identified around countries such as Algeria, Chad, South Africa, Niger, Nigeria, Egypt, Sudan, Ethiopia, Central African Republic, South Sudan, and Libya. The study emphasizes the significance of harnessing solar energy to meet Africa's increasing demand for clean and sustainable energy sources. It provides a GIS analysis of Africa's solar energy potential, estimating the sky-available solar energy. This research offers invaluable insights for engineers, technologists, policymakers, investors, and energy planners by detailing the solar energy potential in various African regions and identifying the wealthiest solar energy clusters. This research supports the promotion of solar energy adoption and the utilization of abundant, untapped solar resources, contributing to a cleaner and more sustainable energy landscape in Africa. The implementation of solar photovoltaic systems in the continent presents a substantial economic opportunity, due to its environmental benefits and relative advantages compared to alternative energy generation methods.