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Export Reference (APA) 
Matos-Carvalho, João P., Stefenon, Stefano Frizzo, Valderi R. Q. Leithardt & Kin-Choong Yow (2025). Time series forecasting based on optimized LLM for fault prediction in distribution power grid insulators. arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles.
Export Reference (IEEE) 
M. J. P. et al.,  "Time series forecasting based on optimized LLM for fault prediction in distribution power grid insulators", in arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles, 2025
Export BibTeX 
@misc{p.2025_1764932281922,
	author = "Matos-Carvalho, João P. and Stefenon, Stefano Frizzo and Valderi R. Q. Leithardt and Kin-Choong Yow",
	title = "Time series forecasting based on optimized LLM for fault prediction in distribution power grid insulators",
	year = "2025",
	doi = "10.48550/arXiv.2502.17341",
	howpublished = "Digital",
	url = "https://arxiv.org/abs/2502.17341"
}
Export RIS 
TY  - GEN
TI  - Time series forecasting based on optimized LLM for fault prediction in distribution power grid insulators
T2  - arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles
AU  - Matos-Carvalho, João P.
AU  - Stefenon, Stefano Frizzo
AU  - Valderi R. Q. Leithardt
AU  - Kin-Choong Yow
PY  - 2025
DO  - 10.48550/arXiv.2502.17341
UR  - https://arxiv.org/abs/2502.17341
AB  - Surface contamination on electrical grid insulators leads to an increase in leakage current until an electrical discharge occurs, which can result in a power system shutdown. To mitigate the possibility of disruptive faults resulting in a power outage, monitoring contamination and leakage current can help predict the progression of faults. Given this need, this paper proposes a hybrid deep learning (DL) model for predicting the increase in leakage current in high-voltage insulators. The hybrid structure considers a multi-criteria optimization using tree-structured Parzen estimation, an input stage filter for signal noise attenuation combined with a large language model (LLM) applied for time series forecasting. The proposed optimized LLM outperforms state-of-the-art DL models with a root-mean-square error equal to 2.24×10−4 for a short-term horizon and 1.21×10−3 for a medium-term horizon.
ER  -