Conference Paper
Proceedings of SimBuild Conference 2024
Simplifying Modeling for Building and District Energy Systems with Large Language Models
Simplifying Modeling for Building and District Energy Systems with Large Language Models
Saman Mostafavi, John T. Maxwell, Maksym Zhenirovskyy, Ion MateiSRI International, United States of AmericaAbstract: We present a modeler-assistant tool designed to facilitate the creation and validation of building and district energy models. Our objective is not to develop a new modeling library but rather to enhance the usability of open-source modeling libraries and the efficiency of the model creation and simulation process. This methodology significantly reduces the manual effort required in model validation. It leverages Large Language Models (LLMs) and Python programming to transform textual description of requirements into Modelica models, complete with graphical annotations for thorough inspection. A key innovation is our API, which leverages syntactically constrained LLMs to streamline user interaction with the Python-based Modelica model generator, ensuring the consistent creation of valid models. Our approach has the potential to reduce manual effort for model validation from hours to minutes, significantly streamlining the process. We provide examples of model generation at various levels of input detail and showcase the integration of weather and operational data to calibrate the models, aligning it more accurately with real-world scenarios. Keywords: Building Energy Modeling, Modelica, Workflow Automation, Large Language Models, District Energy SystemPages: 466 - 474 Paper: