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：本文提出了一种基于大语言模型（LLMs）的自然对话式方法，用于解决个性化能源相关问题。我们聚焦于需要根据建模细微变化反复求解且具有用户特异性的可定制优化问题，这使得构建通用模型面临挑战。我们提出一种将LLM与优化求解器相结合的增强策略，在提升其理解与回应用户规范及偏好的能力同时，提供非线性推理功能。该方法开创性地引入了"人类引导的优化自动形式化"概念，能够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、解释并处理各类实例特定的能源相关问题，突破了当前基于提示词技术的局限性。我们的研究涵盖了能源领域的多种常见任务，从电动汽车充电、暖通空调（HVAC）控制，到长期规划问题（如安装屋顶光伏（PV）或热泵的成本效益评估）。这项初步研究标志着向基于上下文形式化的LLM优化方法迈出了关键一步，有望推动优化过程的民主化。最终，利益相关者能够优化自身能源消耗，推广符合个人需求与偏好的可持续能源实践。