Harnessing AI in renewable energy systems: driving environmental and socio-economic transformation - M. M. Mundu; Mariam Basajja; Emmanuel Kweyu; V. S. Manjula; Daniel Ejim Uti (Springer International Publishing, 2026)
The rapid expansion of renewable energy systems has intensified interest in Artificial Intelligence (AI) for improving forecasting, optimisation, and operational reliability. Yet despite growing technical advances, existing reviews focus narrowly on algorithmic performance and rarely integrate environmental, socio-economic, and governance dimensions, issues that are especially critical for low- and middle-income countries (LMICs), where data scarcity and institutional capacity constraints shape deployment outcomes. This creates an incomplete understanding of the opportunities and risks associated with AI-enabled renewable energy transitions. This study conducts a systematic review, following PRISMA guidelines, to synthesise evidence across four domains: technical integration, environmental impacts, socio-economic implications, and governance considerations. The review examines 113 studies spanning solar, wind, microgrids, storage management, and predictive maintenance. Findings show that while AI can enhance forecasting accuracy and system efficiency, these benefits are highly context-dependent and often derived from simulations rather than field deployments. The literature reveals underexplored risks, including the computational energy footprint of AI models, limited transferability to data-scarce regions, potential reinforcement of inequality in LMIC, and increasing concentration of technological power in corporate actors. Based on these, the paper proposes a cross-sectoral framework for responsible AI adoption in renewable energy and outlines priority actions for researchers, policymakers, and practitioners. These include rigorous reporting of model uncertainty and lifecycle impacts, strengthening data governance and local capacity, and validating AI tools in real-world low-resource contexts. The review concludes that AI can support sustainable energy transitions only when deployed within robust technical, institutional, and equity-oriented governance systems.
Keywords: Artificial intelligence; Renewable energy systems; Grid optimization; Predictive maintenance; Energy access; Socioeconomic impacts; Environmental sustainability