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.

这篇论文介绍了一种利用大型语言模型（LLMs）解决个性化能源相关问题的自然对话方法，通过增加优化求解器，提升 LLMs 对用户需求和偏好的理解和响应能力，将自然语言任务规范自动转化为优化实例，分析、解释和解决各种特定实例的能源相关问题，进而推动可持续能源实践定制化。