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Updated: Jan 10, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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Automated building energy modeling for energy retrofits using a large language model-based multi-agent framework.

Jie Lu1,2, Zeyu Zheng1, Max Langtry2

  • 1Institute of Refrigeration and Cryogenics, Zhejiang University, Hangzhou, China.

Iscience
|November 24, 2025
PubMed
Summary

Data2BEM, a new framework using large language models, automates building energy modeling for retrofits. This tool significantly cuts down modeling time, making energy efficiency upgrades more accessible.

Keywords:
Artificial intelligenceEnergy engineering

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Last Updated: Jan 10, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

1000

Area of Science:

  • Building Science
  • Artificial Intelligence
  • Computational Engineering

Background:

  • Building energy modeling is crucial for effective retrofit design but is often time-consuming.
  • Current methods require significant manual effort and expertise, limiting scalability.

Purpose of the Study:

  • To introduce Data2BEM, a novel framework leveraging large language models (LLMs) and multi-agent systems.
  • To automate the generation and calibration of building energy models from diverse data sources.

Main Methods:

  • Data2BEM parses architectural drawings, specifications, and sensor data.
  • It employs a multi-agent framework for automated model generation and calibration.
  • The system was applied to an office building at the University of Cambridge.

Main Results:

  • Data2BEM generated a calibrated building energy model that met industry accuracy standards.
  • The framework reduced modeling time by over 90% (48 minutes vs. 8-32 hours) compared to professional practice.
  • It successfully enabled the assessment of heat-electrification retrofits.

Conclusions:

  • LLM-driven multi-agent methods can dramatically accelerate building retrofit analysis.
  • Data2BEM lowers expertise and time barriers for practitioners.
  • This approach supports scalable decarbonization strategies for the building sector.