Empowering sustainable energy systems with <scp>AI</scp> : Opportunities, challenges, and strategic solutions

Abstract This article presents a prospective analysis of the application of artificial intelligence (AI), specifically machine learning (ML), in sustainable energy systems. It highlights three significant areas of application: modeling and forecasting, managing energy operations, and detecting faults or unusual activities. As we move toward cleaner, more distributed, and more complex energy systems, new challenges arise—like forecasting demand, coordinating operations, securing energy grids, and planning for the future. AI has the potential to address many of these issues by improving the way we integrate renewable sources, manage energy storage, and detect or prevent system failures, including cyber threats. However, using AI in this field is not without its downsides. For instance, most AI tools need vast data to work well, often lack decision‐making transparency, and struggle when applied to new or different situations. To overcome these hurdles, this article proposes four key strategies: (1) creating synthetic energy data using generative AI, (2) combining AI with physical system knowledge to improve reliability, (3) applying AI to manage complex, interconnected energy systems better, and (4) building multi‐layered cybersecurity systems powered by AI. Altogether, the article offers a fresh look at how AI might shape the future of energy in a smarter, safer, and more sustainable direction.