POTracker: Optimizing Large Language Models for Standard-Compliant Power Outage Report Generation

Researchers have developed POTracker, a fine-tuned large language model optimized for generating machine-readable power outage reports that comply with U.S. energy sector regulatory standards. The model achieves 86.47% structural accuracy and 51% improvement over existing fine-tuning methods by using a novel loss function that balances textual and structural similarity.

POTracker addresses a critical infrastructure challenge where general-purpose LLMs fail to meet domain-specific requirements. Power outage reporting requires strict adherence to regulatory formats and standards—a constraint that extends beyond creative text generation into structured data production. This represents a broader trend in enterprise AI adoption: the gap between impressive general capabilities and practical compliance requirements.

The research tackles a genuine operational bottleneck for utility companies managing nationwide interoperability. Outage reports must be simultaneously accurate, interpretable by both humans and machines, and compliant with energy regulatory bodies' specifications. Current LLMs excel at semantic understanding but struggle with rigid structural constraints, making them unsuitable for critical infrastructure reporting without significant post-processing and validation.

The technical innovation—POTrackerLoss—demonstrates that specialized loss functions can bridge semantic and structural requirements more effectively than off-the-shelf fine-tuning approaches. The 86.47% structural accuracy represents a meaningful threshold for production deployment, while human expert validation averaging 4.03/5.0 suggests the generated reports meet domain quality standards.

For infrastructure operators and energy companies, this research validates that LLMs can handle regulated reporting tasks with proper optimization. The approach may extend beyond power outages to telecommunications, water utilities, and other regulated sectors requiring standardized incident reporting. As enterprises increasingly seek to automate compliance-heavy processes, demonstrating LLM feasibility in these constrained domains becomes commercially significant.

• →POTracker's specialized loss function outperforms standard fine-tuning by up to 51% on power outage report generation.
• →The model achieves 86.47% structural accuracy on regulatory-compliant report formats, enabling practical infrastructure deployment.
• →Domain expert validation (4.03/5.0 score) confirms generated reports meet utility industry quality standards.
• →The approach addresses a critical gap between LLM semantic capabilities and strict enterprise compliance requirements.
• →Results suggest the optimization method could transfer to other regulated sectors requiring standardized data generation.