Journal article
Automatic building energy model development and debugging using large language models agentic workflow
Energy and buildings, v 327, 115116
15 Jan 2025
Abstract
•Explore the use of large language models (LLMs) to automate building energy modeling.•Develop an LLM-agent workflow based on LLM planning for complex procedures.•Accurately translate building description to an EnergyPlus model in a case study.•Outperform naive prompt engineering and other planning in accuracy and reliability.
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 LLM-planning-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.
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Details
- Title
- Automatic building energy model development and debugging using large language models agentic workflow
- Creators
- Liang Zhang - National Laboratory of the RockiesVitaly Ford - Arcadia UniversityZhelun Chen - Tongji UniversityJianli Chen - University of Utah
- Publication Details
- Energy and buildings, v 327, 115116
- Publisher
- Elsevier
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Engineering Management; Civil, Architectural, and Environmental Engineering; School of Engineering
- Web of Science ID
- WOS:001370543900001
- Scopus ID
- 2-s2.0-85210141985
- Other Identifier
- 991022197310804721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Construction & Building Technology
- Energy & Fuels
- Engineering, Civil