Document Type

Article

Publication Date

2026

DOI

10.1007/s10462-026-11545-2

Publication Title

Artificial Intelligence Review

Volume

Advance online publication

Pages

81 pp.

Abstract

The global energy transition toward decarbonization and digitalization requires advanced methods to manage decentralized, data-intensive cyber-physical energy systems. This systematic review analyzes 106 research studies on Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs) in renewable energy and smart grids, organized into seven application clusters covering forecasting, system design, operation, reliability, data and cybersecurity, and energy markets. The review situates these applications within a Cyber-Physical-Social Systems (CPSS) framework. Results show that GANs dominate current applications (47.2%), followed by LLMs (10.4%) and VAEs (9.4%), with growing adoption of diffusion and score-based models (7.5% each). Selected studies report improved probabilistic forecasting and uncertainty calibration using diffusion and score-based approaches, subject to dataset and evaluation setup. GenAI supports system planning through synthetic scenario generation, enhances operational decision support and demand response coordination, and contributes to reliability, cybersecurity, and market analysis. LLMs primarily function as language-driven decision support and knowledge integration components across multiple application domains. Despite computational and data-related constraints, GenAI represents an important enabler of the sustainable digital transition by supporting resilience, adaptability, and governance in renewable energy systems.

Rights

© The Authors 2026.

ess This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if you modified the licensed material. You do not have permission under this license to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

Original Publication Citation

Cali, U., Halden, U., Andoni, M., Catak, F. O., Chen, S., Couraud, B., Kantar, E., Knapper, S., Kucukdemiral, I., Kusetogullari, H., Kuzlu, M., Mousavi, Y., Norbu, S., Ustun, T. S., & Flynn, D. (2026). Generative AI and LLM applications in renewable energy and smart grids: a systematic review for the sustainable energy transition. Artificial Intelligence Review. Advance online publication. https://doi.org/10.1007/s10462-026-11545-2

ORCID

0000-0002-8719-2353 (Kuzlu)

Download

Share

COinS