Building Management System (BMS) through a data-driven method always faces data and model scalability issues. We propose a methodology to tackle the scalability challenges associated with the development of data-driven models for BMS by using Large Language Models (LLMs). LLMs' code generation adaptability can enable broader adoption of BMS by "automating the automation," particularly the data handling and data-driven modeling processes. In this paper, we use LLMs to generate code that processes structured data from BMS and build data-driven models for BMS's specific requirements. This eliminates the need for manual data and model development, reducing the time, effort, and cost associated with this process. Our hypothesis is that LLMs can incorporate domain knowledge about data science and BMS into data processing and modeling, ensuring that the data-driven modeling is automated for specific requirements of different building types and control objectives, which also improves accuracy and scalability. We generate a prompt template following the framework of Machine Learning Operations so that the prompts are designed to systematically generate Python code for data-driven modeling. Our case study indicates that bi-sequential prompting under the prompt template can achieve a high success rate of code generation and code accuracy, and significantly reduce human labor costs.

翻译：通过数据驱动方法构建建筑管理系统（BMS）始终面临数据与模型的可扩展性问题。本文提出一种利用大语言模型（LLMs）应对BMS数据驱动模型开发过程中可扩展性挑战的方法论。LLMs的代码生成适应性可通过“自动化自动化过程”——特别是数据处理与数据驱动建模流程——推动BMS的更广泛采用。本文使用LLMs生成处理BMS结构化数据的代码，并根据BMS特定需求构建数据驱动模型。该方法无需人工进行数据与模型开发，显著减少了相关时间、人力与成本投入。我们的核心假设是：LLMs能够将数据科学与BMS领域知识融入数据处理与建模过程，确保针对不同建筑类型与控制目标的数据驱动建模实现自动化，同时提升模型精度与可扩展性。我们依据机器学习运维框架构建提示模板，通过系统化设计的提示生成适用于数据驱动建模的Python代码。案例研究表明，基于该提示模板的双序列提示策略能够实现较高的代码生成成功率与代码准确率，并显著降低人力成本。