Global advances in Generative AI for renewable energy and implications for sustainable energy transitions in Africa

Generative Artificial Intelligence (GenAI) is being investigated for renewable-energy forecasting, optimisation, predictive maintenance, synthetic-data generation, and infrastructure planning. This study reviews global advances and critically assesses their implications for Africa's sustainable energy transition. It aims to: (i) map GenAI applications in renewable-energy systems; (ii) distinguish demonstrated results from proposed African opportunities; (iii) evaluate evidence quality, transferability, and deployment feasibility; and (iv) identify context-specific research and policy priorities. A systematic review was reported using PRISMA 2020, with the PRALSMA AI-assisted workflow used to support, but not replace, researcher-led screening and synthesis. OpenAlex records published from 2020 to May 2026 were screened, yielding 75 included studies. Manual analysis was triangulated with ChatGPT Project and NotebookLM, and findings were weighted by methodological quality, evidence maturity, and relevance to African data, grid, infrastructure, institutional, and cost conditions. The corpus remains geographically imbalanced: five of the 75 included studies report an African institutional affiliation (Morocco, Nigeria, and South Africa, with further co-affiliations in Tunisia and Gabon), and most quantitative outcomes still derive from non-African simulations, prototypes, or case studies. Consequently, reported error, cost, emission, and accuracy improvements are treated as source-specific rather than directly transferable African effects. Near-term priorities are controlled pilots of lightweight, explainable, offline-capable decision support and synthetic-data methods; compute-intensive diffusion, agentic, digital-twin, and imitation-learning applications require further validation. GenAI may support African renewable-energy transitions, but benefits depend on local datasets, reliable fallback procedures, lifecycle environmental assessment, workforce capacity, governance, and staged field validation.