The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
EPlus-LLM: A large language model-based computing platform for automated building energy modeling
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Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“A key limitation is that the proposed system is targeting simple rectangular building geometries limiting its generalizability to more complex scenarios.”
From A review of LLMs and their applications in the architecture, engineering and construction industry (2025) · cites this paper as Jiang et al. (2024b)
In the citing paper
“The authors also highlighted the significant computational requirements for deploying and fine-tuning the model.”
From A review of LLMs and their applications in the architecture, engineering and construction industry (2025) · cites this paper as Jiang et al. (2024b)
In the citing paper
“while resistant to minor prompt variations, the performance of the model still depends on the clarity and quality of the prompts.”
From A review of LLMs and their applications in the architecture, engineering and construction industry (2025) · cites this paper as Jiang et al. (2024b)
Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
Deep Contextualized Word Representations
Peters, M., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · doi:10.18653/v1/n18-1202 · Cited by 11,983
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., & Liu, P. J. (2019) · arXiv · doi:10.48550/arxiv.1910.10683 · Cited by 10,929
LLaMA: Open and Efficient Foundation Language Models
Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al. (2023) · arXiv · doi:10.48550/arxiv.2302.13971 · Cited by 4,602
Training language models to follow instructions with human feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C. L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al. (2022) · arXiv · doi:10.48550/arxiv.2203.02155 · Cited by 4,208
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., & Darrell, T. (2013) · arXiv · doi:10.48550/arxiv.1310.1531 · Cited by 4,019
Deep Learning using Rectified Linear Units (ReLU)Preprint
Agarap, A. F. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.08375 · Cited by 3,792
PaLM: Scaling Language Modeling with Pathways
Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H. W., Sutton, C., Gehrmann, S., et al. (2022) · arXiv · doi:10.48550/arxiv.2204.02311 · Cited by 2,590
Cited by
Sorted by citations
Survey on large language model-enhanced reinforcement learning: Concept, taxonomy, and methods
Cao, Y., Zhao, H., Cheng, Y., Shu, T., Chen, Y., Liu, G., Liang, G., Zhao, J., Yan, J., & Li, Y. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3497992 · Cited by 174
Large language models for building energy applications: Opportunities and challenges
Liu, M., Zhang, L., Chen, J., Chen, W.-A., Yang, Z., Lo, L. J., Wen, J., & O’Neill, Z. D. (2025) · Building Simulation · doi:10.1007/s12273-025-1235-9 · Cited by 116
Self-citationA review of physics-informed machine learning for building energy modeling
Ma, Z., Jiang, G. Y., Hu, Y., & Chen, J. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.125169 · Cited by 111
Self-citationAdvancing building energy modeling with large language models: Exploration and case studies
Zhang, L., Chen, Z., & Ford, V. (2024) · Energy and Buildings · doi:10.1016/j.enbuild.2024.114788 · Cited by 101
Self-citationXiao, T., & Xu, P. (2024) · Energy and Buildings · doi:10.1016/j.enbuild.2024.114691 · Cited by 86
Metadata
- Authors
- Jiang, G., Ma, Z., Zhang, L., & Chen, J.
- Affiliations
- University of Utah (US)
- University of Arizona (US)
- Corresponding author
- Chen, Jianli
- Venue
- Applied Energy
- Publisher
- Elsevier BV
- Published
- 2024-08
- Volume
- 367
- Pages
- 123431
- ISSN
- 0306-2619 (ISSN-L)
- OpenAlex
- W4396986140
- Cited by
- 204
- References
- 30
- Citations by year
- 2026: 120 · 2025: 75 · 2024: 9
- FWCI
- 34.99
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (am, 288 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Applied Energy (acceptedVersion)
- SSRN Electronic Journal (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- National Science Foundation (2311685)
- 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, EnergyPlus, T5 fine-tuning, human-AI interface, urban building energy modeling, prompt robustness
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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