Automatic building energy model development and debugging using large language models agentic workflow

Liang Zhang

doi:10.1016/j.enbuild.2024.115116

Automatic building energy model development and debugging using large language models agentic workflow

2025-01-01

1

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AI

DOI：10.1016/j.enbuild.2024.115116

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Building energy modeling (BEM) is a complex process that demands significant time and expertise, limiting its broader application in building design and operations. While Large Language Models (LLMs) agentic workflow have facilitated complex engineering processes, their application in BEM has not been specifically explored. This paper investigates the feasibility of automating BEM using LLM agentic workflow. We developed a generic LLMplanning-based workflow that takes a building description as input and generates an error-free EnergyPlus building energy model. Our robust workflow includes four core agents: 1) Building Description Pre-Processing, 2) IDF Object Information Extraction, 3) Single IDF Object Generator Suite, and 4) IDF Debugging Agent. These agents divide the complex tasks into manageable sub-steps, enabling LLMs to generate accurate and reliable results at each stage. The case study demonstrates the successful translation of a building description into an error-free EnergyPlus model for the iUnit modular building at the National Renewable Energy Laboratory. The effectiveness of our workflow surpasses: 1) naive prompt engineering, 2) other LLM-based workflows, and 3) manual modeling, in terms of accuracy, reliability, and time efficiency. The paper concludes with a discussion on the interplay between foundational models and LLM agent planning design, advocating for the use of fine-tuned, specialized models to advance this field.

Keyword:

Building energy modeling

Complex system modeling

Large language model

Generative artificial intelligence

Agentic workflow

Automatic building energy model development and debugging using large language models agentic workflow

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