Climate Modeling for Renewable Energy Potential in Africa Using Advanced Modeling

Climate Modeling for Renewable Energy Potential in Africa Using Advanced Modeling | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Climate Modeling for Renewable Energy Potential in Africa Using Advanced Modeling Tayo P. Ogundunmade, Adedayo A. Adepoju This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9234286/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Despite the availability of abundant sources of renewable energy such as solar, wind, hydro, and geothermal energy, African development faces challenges related to the variability of resource availability and lack of infrastructure. This study focuses on the application of advanced modeling techniques such as ensemble models, machine learning models, Geographic Information System (GIS) models, and hydro models in assessing the potential of renewable energy development in Africa. The study reveals the benefits of employing ensemble modeling to reduce uncertainty levels through providing a probable distribution of resource availability. Machine learning aids in improving the accuracy of predictions through local dynamics. GIS provides contextual information to resource availability through integrating social, economic, and infrastructure information. Hydrological modeling provides quantified risks related to climate change, especially for hydroelectric power. The findings reveal regional strengths, including North Africa as a solar powerhouse, East Africa as a balanced source of wind and hydroelectric power, Southern Africa as a source of diversification opportunities, and West Africa as a source of modest solar and hydroelectric power and limited wind power. A comparative study reveals the importance of energy planning tailored to regional requirements rather than adopting a generic approach. The findings emphasize the importance that should be given to the concept of resilience in energy planning, given that climate change is likely to escalate these extreme events to unprecedented levels. The policy implications are significant, emphasizing the importance that should be given to investing in data infrastructure, capacity building, and cooperation among different countries. Climate Analysis and Modeling Climate modelling renewable energy Africa regional climate models sustainable development Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9234286","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":612674353,"identity":"c26469da-b39c-4119-bd55-d48f2c9a768c","order_by":0,"name":"Tayo P. 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