The role of artificial intelligence in accelerating renewable energy adoption for global energy transformation - Sameer Algburi; Salah Sabeeh Abed Al Kareem; I.B. Sapaev; Otabek Mukhitdinov; Qusay Hassan; Doaa H. Khalaf; Feryal Ibrahim Jabbar (Unconventional Resources)
Artificial Intelligence (AI) has emerged as a critical solution to address persistent challenges hindering renewable energy adoption, including resource intermittency, grid integration complexities, and economic barriers. This review synthesizes recent advancements, highlighting AI's capability to significantly enhance renewable energy systems through improved forecasting accuracy, optimized resource allocation, and heightened operational efficiency. The findings demonstrate AI-driven predictive models' effectiveness in aligning energy generation with demand, reducing operational downtime via predictive maintenance, and stabilizing energy distribution in AI-powered smart grids. Further, AI facilitates efficient management of decentralized energy networks, including microgrids, and enhances energy storage solutions to maintain reliability during low-generation periods. AI's contribution to refining electrolysis processes significantly boosts green hydrogen viability, offering promising decarbonization pathways for energy-intensive industries. Evidence from various international case studies underscores AI's transformative impact, notably in wind and solar forecasting and hybrid system optimization, driving cost reductions and broadening renewable energy access, especially in developing regions. The paper suggests prioritizing research on fully autonomous smart grids and advanced storage solutions to further enhance scalability, reliability, and support global Net-Zero ambitions. Additionally, addressing the societal and environmental implications of AI deployment remains essential for maximizing its sustainable impact in transforming the global energy landscape.