This research introduces a novel approach to generate synthetic household energy data using Large Language Models, addressing critical privacy concerns and data limitations in smart-grid applications.

• Compares five different LLMs for creating realistic, culturally-sensitive energy usage patterns
• Enables the development of more robust ML models for energy sector applications
• Creates diverse, geographically-specific data that reflects actual household behaviors
• Reduces barriers to ML adoption in the energy industry by mitigating data privacy issues

This engineering innovation matters because it provides a practical solution to a significant bottleneck in smart-grid research, potentially accelerating the deployment of AI-driven energy optimization solutions while respecting user privacy.

Knowledge Distillation from Large Language Models for Household Energy Modeling