Advancing Building Energy Modeling with Large Language Models: Exploration and Case Studies

TL;DR

This paper explores integrating large language models like ChatGPT with building energy modeling software, demonstrating their potential to automate, optimize, and enhance energy efficiency in building simulations through case studies.

Contribution

It introduces novel methods for applying large language models to building energy modeling, highlighting their capabilities in automation and optimization.

Findings

Large language models can automate energy modeling tasks.

Integration of LLMs improves simulation efficiency.

Case studies show enhanced accuracy and reduced effort.

Abstract

The rapid progression in artificial intelligence has facilitated the emergence of large language models like ChatGPT, offering potential applications extending into specialized engineering modeling, especially physics-based building energy modeling. This paper investigates the innovative integration of large language models with building energy modeling software, focusing specifically on the fusion of ChatGPT with EnergyPlus. A literature review is first conducted to reveal a growing trend of incorporating large language models in engineering modeling, albeit limited research on their application in building energy modeling. We underscore the potential of large language models in addressing building energy modeling challenges and outline potential applications including simulation input generation, simulation output analysis and visualization, conducting error analysis, co-simulation, simulation knowledge extraction and training, and simulation optimization. Three case studies reveal the transformative potential of large language models in automating and optimizing building energy modeling tasks, underscoring the pivotal role of artificial intelligence in advancing sustainable building practices and energy efficiency. The case studies demonstrate that selecting the right large language model techniques is essential to enhance performance and reduce engineering efforts. The findings advocate a multidisciplinary approach in future artificial intelligence research, with implications extending beyond building energy modeling to other specialized engineering modeling.