This paper explores the transformative role of artificial intelligence (AI) in modernizing energy systems to enhance efficiency, sustainability, and resilience in response rising global energy demands and environmental concerns. A comprehensive literature review and systematic analysis highlight how AI-driven innovations impact energy generation, distribution, consumption, and system operations. Key AI techniques such as machine learning, deep learning, reinforcement learning, and optimization algorithms play pivotal roles in enhancing renewable energy integration, optimizing smart grid functionalities, improving energy storage solutions, and redefining energy market dynamics. A structured approach was presented for AI-based energy system transformation, emphasizing the importance of setting clear goals, selecting strategic directions, implementing AI applications, and establishing continuous feedback loops for verification and improvement. By segmenting the energy system into functional components, production, transportation, distribution, consumption, and system operations, an in-depth analysis of AI applications was provided relevant to each segment. Specific AI technologies, models, and algorithms suitable for various applications are identified, along with associated challenges and considerations. AI is a transformative force capable of reshaping energy systems to meet contemporary demands for sustainability and efficiency. By thoughtfully integrating AI technologies across the various components of energy systems and addressing associated challenges, the energy sector can unlock new levels of performance and innovation, contributing significantly to global sustainability objectives. Keywords: energy system, energy security, energy transformation, energy market, energy services, artificial intelligence.
Karpenko et al. (Wed,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: