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Early detection of power outages is crucial for maintaining a reliable powerdistribution system. This research investigates the use of transfer learningand language models in detecting outages with limited labeled data. Byleveraging pretraining and transfer learning, models can generalize to unseenclasses.  Using a curated balanced dataset of social media tweets related to poweroutages, we conducted experiments using zero-shot and few-shot learning. Ourhypothesis is that Language Models pretrained with limited data could achievehigh performance in outage detection tasks over baseline models. Results showthat while classical models outperform zero-shot Language Models, few-shotfine-tuning significantly improves their performance. For example, with 10%fine-tuning, BERT achieves 81.3% accuracy (+15.3%), and GPT achieves 74.5%accuracy (+8.5%). This has practical implications for analyzing and localizingoutages in scenarios with limited data availability.  Our evaluation provides insights into the potential of few-shot fine-tuningwith Language Models for power outage detection, highlighting their strengthsand limitations. This research contributes to the knowledge base of leveragingadvanced natural language processing techniques for managing criticalinfrastructure.

Transfer Learning for Power Outage Detection Task with Limited Training Data