Digital twin technology and artificial intelligence in energy transition: A comprehensive systematic review of applications

The transition to reliable, affordable, and sustainable energy is a continuing global challenge still shaped by the goals of carbon neutrality and mitigation of environmental impact. Achieving this transition will require a high degree of management skill, underpinned by advanced digital technologies, and new practice approaches. The use of Digital Twin (DT) technology, combined with Artificial Intelligence (AI) revolutionizing the management, maintenance, and real-time monitoring of renewable energy systems. This systematic literature review follows the PRISMA methodology to analyze 42 high-impact studies, providing a comprehensive synthesis of DT applications in the energy sector. The results reveal that AI-powered DT models enhance predictive maintenance efficiency leading to a 35 % reduction in unplanned downtime an 8.5 % increase in energy production, 98.3 % accuracy in fault detection and a 26.2 % reduction in energy costs. However several challenges remain, including high implementation costs, cybersecurity risks, and the complexity of integration. This study provides a clear perspective on this technology its applications, and the solutions it offers. It highlights existing challenges and future directions for leveraging digital techniques to accelerate the transition towards intelligent and sustainable energy systems.