Generative AI-based spatiotemporal resilience, green and low-carbon transformation strategy of smart renewable energy systems

Abstract

The extensive integration of AI with renewable energy systems is a major trend in technological advancement, but its energy consumption and carbon emissions are also a major challenge. Generative AI can quickly generate human-like content responding to cues, with excellent reasoning and generative capabilities. Generative AI-based renewable energy systems can cope with dynamic system changes and have great potential for resilience optimization and green low-carbon transition. In this paper, we first explore the role that generative AI can play in renewable energy systems and explain shock incidents. Secondly, intelligent maintenance strategies of renewable energy systems under different failure modes are developed based on generative AI. Then spatiotemporal resilience is introduced and a spatiotemporal resilience optimization model is proposed. A green and low-carbon transformation strategy for smart renewable energy systems has also been proposed. Finally, a case study is used to illustrate the utilization of the proposed method by using a wind power system as an example of a renewable energy system.

Graphical abstract

Keywords

generative AI / spatiotemporal resilience / carbon transformation / smart renewable energy

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Competing Interests

The authors declare that they have no competing interests.

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2025 Higher Education Press