Machine learning-driven renewable energy grid integration stability assessment: LIME interpretability and LLM intelligent analysis.
- DOI
- 10.1038/s41598-026-48087-0
- Published
- 2026 Apr 27
- Container
- Scientific reports
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.1038/s41598-026-48087-0,
title = {Machine learning-driven renewable energy grid integration stability assessment: LIME interpretability and LLM intelligent analysis.},
author = {Xu R and Zheng Y and Meng Q and Yang Q},
year = {2026},
journal = {Scientific reports},
doi = {10.1038/s41598-026-48087-0},
url = {https://doi.org/10.1038/s41598-026-48087-0}
}RIS
TY - JOUR TI - Machine learning-driven renewable energy grid integration stability assessment: LIME interpretability and LLM intelligent analysis. AU - Xu R AU - Zheng Y AU - Meng Q AU - Yang Q PY - 2026 JO - Scientific reports DO - 10.1038/s41598-026-48087-0 UR - https://doi.org/10.1038/s41598-026-48087-0 ER -
APA
R, X., Y, Z., Q, M., & Q, Y. (2026). Machine learning-driven renewable energy grid integration stability assessment: LIME interpretability and LLM intelligent analysis.. Scientific reports. https://doi.org/10.1038/s41598-026-48087-0
Source records
- pubmed · retrieved 2026-09-25T23:10:03.818Z