Large language model-based agent Schema and library for automated building energy analysis and modeling
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Agents can take on responsibility for building energy work that involves data processing, coding, and simulations.
“Agents can take on responsibility for building energy work that now involves data processing, coding, and simulations [58].”
From The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges (2025) · cites this paper as [58]
CSV files can provide AI with inputs and can be used in combination with pretrained ML models to cue AI to produce and debug the energy modeling code with tools like LangGraph with ReAct and PythonREPL.
“CSV files can provide AI with inputs and can be used in combination with pretrained ML models to cue AI to produce and debug the energy modeling code with tools like LangGraph with ReAct and PythonREPL [58].”
From The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges (2025) · cites this paper as [58]
Retrieval-augmented generation, tree-of-thought backtracking, and iterative debugging can help rectify hallucinations and task-related mistakes.
“while tools like retrieval-augmented generation, tree-of-thought backtracking, and iterative debugging can help rectify hallucinations and task-related mistakes [58,75,82,132]”
From The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges (2025) · cites this paper as [58]
Model implementations LangGraph, Autogen, and GoEX averaged 12 ± 2 min TCT, which is a 34.2% improvement against traditional AI in general.
“Model implementations LangGraph [58], Autogen [151], and GoEX [53] averaged 12 ± 2 min TCT, which is a 34.2% improvement against traditional AI in general.”
From The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges (2025) · cites this paper as [58]
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- Authors
- Zhang, L., Fu, X., Li, Y., & Chen, J.
- Affiliations
- National Laboratory of the Rockies (US)
- University of Arizona (US)
- Oak Ridge National Laboratory (US)
- Tongji University (CN)
- Corresponding author
- Zhang, Liang
- Venue
- Automation in Construction
- Publisher
- Elsevier BV
- Published
- 2025-08
- Volume
- 176
- Pages
- 106244
- ISSN
- 0926-5805 (ISSN-L)
- OpenAlex
- W4410080756
- Cited by
- 40
- References
- 39
- Citations by year
- 2026: 32 · 2025: 8
- FWCI
- 14.59
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Automation in Construction (publishedVersion)
- Topics
- Physical Sciences › Engineering › Building and Construction › Building Energy and Comfort Optimization
- Physical Sciences › Engineering › Building and Construction › BIM and Construction Integration
- Physical Sciences › Engineering › Building and Construction › Building energy efficiency and sustainability
- Keywords
- large language models, LLM agents, JSON Schema, building energy simulation, building energy analysis, open-source library, reproducibility, agent interoperability
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