Machine learning-driven renewable energy grid integration stability assessment: LIME interpretability and LLM intelligent analysis
- DOI
- 10.1038/s41598-026-48087-0
- Published
- 2026-04-27
- Container
- Scientific Reports
- Publisher
- Springer Science and Business Media LLC
- Open access
- unknown
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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 = {Rui Xu and Yu Zheng and Qisong Meng and Qiyu Yang},
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 - Rui Xu AU - Yu Zheng AU - Qisong Meng AU - Qiyu Yang PY - 2026 JO - Scientific Reports DO - 10.1038/s41598-026-48087-0 UR - https://doi.org/10.1038/s41598-026-48087-0 ER -
APA
Xu, R., Zheng, Y., Meng, Q., & Yang, Q. (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
- crossref · retrieved 2026-11-10T13:05:19.529Z