Democratizing Energy Management with LLM-Assisted Optimization Autoformalism

This paper introduces a method for personalizing energy optimization using large language models (LLMs) combined with an optimization solver. This approach, termed human-guided optimization autoformalism, translates natural language specifications into optimization problems, enabling LLMs to handle various user-specific energy-related tasks. It allows for nuanced understanding and nonlinear reasoning tailored to individual preferences. The research covers common energy sector tasks like electric vehicle charging, HVAC control, and long-term planning for renewable energy installations. This novel strategy represents a significant advancement in context-based optimization using LLMs, facilitating sustainable energy practices customized to individual needs.