This paper outlines a natural conversational approach to solving personalized energy-related problems using large language models (LLMs). We focus on customizable optimization problems that necessitate repeated solving with slight variations in modeling and are user-specific, hence posing a challenge to devising a one-size-fits-all model. We put forward a strategy that augments an LLM with an optimization solver, enhancing its proficiency in understanding and responding to user specifications and preferences while providing nonlinear reasoning capabilities. Our approach pioneers the novel concept of human-guided optimization autoformalism, translating a natural language task specification automatically into an optimization instance. This enables LLMs to analyze, explain, and tackle a variety of instance-specific energy-related problems, pushing beyond the limits of current prompt-based techniques. Our research encompasses various commonplace tasks in the energy sector, from electric vehicle charging and Heating, Ventilation, and Air Conditioning (HVAC) control to long-term planning problems such as cost-benefit evaluations for installing rooftop solar photovoltaics (PVs) or heat pumps. This pilot study marks an essential stride towards the context-based formulation of optimization using LLMs, with the potential to democratize optimization processes. As a result, stakeholders are empowered to optimize their energy consumption, promoting sustainable energy practices customized to personal needs and preferences.

翻译：本文概述了一种利用大语言模型（LLMs）解决个性化能源问题的自然对话式方法。我们聚焦于需要根据微调需求反复求解、且用户特定的可定制优化问题，这给构建通用模型带来了挑战。我们提出一种策略，将优化求解器与LLM相结合，在增强其非线性推理能力的同时，提升其对用户规范和偏好的理解与响应能力。该方法开创了人机引导优化自动形式化的新概念，将自然语言任务规范自动转化为优化实例。这使得LLM能够分析、解释并处理多种实例化的能源相关问题，突破了当前基于提示词技术的局限。本研究涵盖能源领域的多种常见任务，从电动汽车充电与供暖通风空调（HVAC）控制，到长期规划问题（如屋顶光伏系统或热泵安装的成本效益评估）。这项探索性研究标志着向基于上下文的LLM优化形式化迈出的关键一步，有望推动优化过程的民主化。最终，利益相关者能够自主优化其能源消耗，从而促进契合个人需求与偏好的可持续能源实践。