Artificial Intelligence for Sustainable Energy Transition: Opportunities, Challenges, and Systems Perspectives

The global pursuit of carbon neutrality and sustainable development is accelerating the transformation of energy systems toward cleaner, more efficient, resilient, and interconnected infrastructures. Renewable energy systems, including solar, wind, biomass, hydropower, and marine energy, are becoming central to this transition, while electrification, digitalization, and sector coupling are reshaping the planning and operation of modern energy systems. However, the large-scale integration of renewable energy introduces significant challenges related to intermittency, forecasting uncertainty, operational flexibility, energy storage, and system reliability. Artificial intelligence (AI) has emerged as a transformative technology capable of addressing these challenges through advanced forecasting, intelligent optimization, autonomous control, and data-driven decision-making. At the same time, the rapid expansion of AI applications—particularly foundation models, cloud computing, and data centers—is driving substantial growth in electricity demand and associated environmental impacts, creating new challenges for sustainable energy development. Understanding AI as both an enabler of and a consumer within sustainable energy systems is therefore becoming increasingly important for achieving long-term low-carbon transitions.This Research Topic aims to explore the multifaceted relationship between artificial intelligence and sustainable energy transition by examining both the opportunities enabled by AI-driven innovation and the emerging sustainability challenges associated with AI deployment. We seek interdisciplinary contributions that integrate artificial intelligence, energy engineering, environmental systems, and sustainability science to advance intelligent, low-carbon, and resilient energy systems. Particular emphasis will be placed on AI-enabled forecasting, optimization, operation, and control of renewable energy systems, smart grids, integrated energy systems, distributed energy resources, and energy storage technologies. We also encourage research on digital twins, machine learning, deep learning, foundation models, intelligent optimization, and autonomous decision-making for improving system flexibility, operational efficiency, and reliability. In addition, this Research Topic welcomes studies addressing the growing energy demand and carbon footprint of AI infrastructures, including green AI, energy-efficient computing, sustainable data centers, lifecycle environmental assessment, and resource-efficient AI architectures. By bridging advances in artificial intelligence with sustainable energy and environmental systems, this collection aims to provide innovative methodologies, practical solutions, and systems-level perspectives that support a resilient, efficient, and environmentally sustainable energy transition.This Research Topic welcomes Original Research Articles, Review Articles, Mini Reviews, Perspectives, Methods, and Case Studies. Topics of interest include, but are not limited to:• AI-driven forecasting, optimization, and intelligent operation of renewable energy systems, integrated energy systems, and smart grids; • Machine learning, deep learning, foundation models, digital twins, and intelligent decision-support technologies for sustainable energy systems; • AI applications in energy storage, demand response, distributed energy resources, and flexible low-carbon energy systems; • Green AI, energy-efficient computing, sustainable data centers, and environmentally responsible AI infrastructures; • Carbon footprint assessment, lifecycle assessment, and environmental impacts of AI-enabled energy systems; • Systems engineering, optimization, and digital technologies for sustainable energy and environmental systems; • AI-enabled industrial decarbonization, resource efficiency, circular energy systems, and low-carbon manufacturing; • Policy, governance, market mechanisms, and sustainability assessment supporting AI-enabled energy transition. We particularly encourage interdisciplinary contributions that combine artificial intelligence, sustainable energy systems, environmental systems engineering, optimization, and digital technologies. Submissions should demonstrate methodological innovation, engineering significance, and practical relevance to advancing resilient, low-carbon, and environmentally sustainable energy systems. Topic editors: Yongjing Xie, Aoxiang Zhang, Kejia Yan Source: https://www.frontiersin.org/research-topics/84105/artificial-intelligence-for-sustainable-energy-transition-opportunities-challenges-and-systems-perspectives