Scaling Data-Driven Building Energy Modelling using Large Language Models

TL;DR

This paper presents a methodology using Large Language Models to automate data handling and modeling in Building Management Systems, enhancing scalability, reducing manual effort, and improving accuracy.

Contribution

It introduces a prompt-based approach leveraging LLMs for automated code generation in BMS data-driven modeling, addressing scalability and efficiency issues.

Findings

High success rate of code generation with bi-sequential prompting

Significant reduction in human labor costs

Improved scalability and accuracy in BMS modeling

Abstract

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.