EPlus-LLM: A large language model-based computing platform for automated building energy modeling

Deep Residual Learning for Image Recognition

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Attention Is All You Need

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Deep Contextualized Word Representations

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Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

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LLaMA: Open and Efficient Foundation Language Models

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Training language models to follow instructions with human feedback

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DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition

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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 · see citing works

PaLM: Scaling Language Modeling with Pathways

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Attention mechanisms in computer vision: A survey

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CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation

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CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation

Wang, Y., Wang, W., Joty, S., & Hoi, S. C. H. (2021) · arXiv · doi:10.48550/arxiv.2109.00859 · Cited by 1,495 · see citing works

Scaling Instruction-Finetuned Language Models

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wav2vec: Unsupervised Pre-Training for Speech Recognition

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FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness

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Impact of fully connected layers on performance of convolutional neural networks for image classification

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Massive Exploration of Neural Machine Translation Architectures

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CodeT5+: Open Code Large Language Models for Code Understanding and Generation

Wang, Y., Le, H., Gotmare, A., Bui, N., Li, J., & Hoi, S. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.68 · Cited by 459 · see citing works

CodeT5+: Open Code Large Language Models for Code Understanding and Generation

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Pre-Trained Language Models and Their Applications

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DeST — An integrated building simulation toolkit Part I: Fundamentals

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Computer-aided building energy analysis techniques

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Chemical language models for de novo drug design: Challenges and opportunities

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Review on occupancy detection and prediction in building simulation

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Latin hypercube sampling with inequality constraints

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Efficient Streaming Language Models with Attention Sinks

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SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-Scaling

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Migratable urban street scene sensing method based on vision language pre-trained model

Zhang, Y., Zhang, F., & Chen, N. (2022) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2022.102989 · Cited by 44 · see citing works

Mixture-of-Experts Meets Instruction Tuning:A Winning Combination for Large Language ModelsPreprint

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A review of validation methods for building energy modeling programs

Zhou, X., Liu, R., Tian, S., Shen, X., Yang, X., An, J., & Yan, D. (2023) · Building Simulation · doi:10.1007/s12273-023-1050-0 · Cited by 19 · see citing works

Financial News Analytics Using Fine-Tuned Llama 2 GPT ModelPreprint

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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

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 · see citing works

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 110 · see citing works

A 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 104 · see citing works

Advancing 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 94 · see citing works

Exploring automated energy optimization with unstructured building data: A multi-agent based framework leveraging large language models

Xiao, T., & Xu, P. (2024) · Energy and Buildings · doi:10.1016/j.enbuild.2024.114691 · Cited by 80 · see citing works

Automatic building energy model development and debugging using large language models agentic workflow

Zhang, L., Ford, V., Chen, Z., & Chen, J. (2024) · Energy and Buildings · doi:10.1016/j.enbuild.2024.115116 · Cited by 79 · see citing works

A systematic review of transformers and large language models in the energy sector: towards agentic digital twins

Antonesi, G., Cioara, T., Anghel, I. M., Michalakopoulos, V., Sarmas, E., & Toderean, L. (2025) · Applied Energy · doi:10.1016/j.apenergy.2025.126670 · Cited by 102 · see citing works

Domain-specific large language models for fault diagnosis of heating, ventilation, and air conditioning systems by labeled-data-supervised fine-tuning

Zhang, J., Zhang, C., Lu, J., & Zhao, Y. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.124378 · Cited by 66 · see citing works

Prompt engineering to inform large language model in automated building energy modeling

Jiang, G., Ma, Z., Zhang, L., & Chen, J. (2025) · Energy · doi:10.1016/j.energy.2025.134548 · Cited by 63 · see citing works

A review of LLMs and their applications in the architecture, engineering and construction industry

Kampelopoulos, D., Tsanousa, A., Vrochidis, S., & Kompatsiaris, I. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11241-7 · Cited by 69 · see citing works

A Review of Large Language Models for Energy Systems: Applications, Challenges, and Future Prospects

Mirshekali, H., Shadi, M. R., Ladani, F. G., & Shaker, H. R. (2025) · IEEE Access · doi:10.1109/access.2025.3610994 · Cited by 57 · see citing works

Applications of multimodal large language models in construction industry

Erfani, A., & Mansouri, A. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.103909 · Cited by 50 · see citing works

Text2BIM: Generating Building Models Using a Large Language Model-Based Multiagent Framework

Du, C., Esser, S., Nousias, S., & Borrmann, A. (2025) · Journal of Computing in Civil Engineering · doi:10.1061/jccee5.cpeng-6386 · Cited by 48 · see citing works

Industrial Foundation Models (IFMs) for intelligent manufacturing: A systematic review

Zhao, S., Liu, S., Jiang, Y., Zhao, B. M., Lv, Y., Zhang, J., Wang, L., & Zhong, R. Y. (2025) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2025.06.011 · Cited by 46 · see citing works

Efficient fine-tuning of large language models for automated building energy modeling in complex cases

Jiang, G., & Chen, J. (2025) · Automation in Construction · doi:10.1016/j.autcon.2025.106223 · Cited by 44 · see citing works

A large language model-based building operation and maintenance information query

Li, Y., Ji, M., Chen, J., Wei, X., Xiao-hong, G., & Tang, J. (2025) · Energy and Buildings · doi:10.1016/j.enbuild.2025.115515 · Cited by 43 · see citing works

Large language model-based agent Schema and library for automated building energy analysis and modeling

Zhang, L., Fu, X., Li, Y., & Chen, J. (2025) · Automation in Construction · doi:10.1016/j.autcon.2025.106244 · Cited by 40 · see citing works

Large Language Models Meet Energy Systems: Opportunities, Challenges, and Future Perspectives

Zhang, C., Zhang, J., Lü, J., & Zhao, Y. (2025) · Applied Energy · doi:10.1016/j.apenergy.2025.127076 · Cited by 43 · see citing works

Review of optimization control methods for HVAC systems in Demand Response (DR): Transition from model-driven to model-free approaches and challenges

Jin, R., Xu, P., Gu, J., Xiao, T., Li, C., & Wang, H. (2025) · Building and Environment · doi:10.1016/j.buildenv.2025.113045 · Cited by 38 · see citing works

Ontology-enabled AI agent-driven intelligent digital twins for building operations and maintenance

Yoon, S., Song, J., & Li, J. (2025) · Journal of Building Engineering · doi:10.1016/j.jobe.2025.112802 · Cited by 36 · see citing works

PowerMistral: A data-efficient wind power forecasting framework leveraging pre-trained large language models

Meng, Q., Gao, Y., He, Y., Hussain, S., Lu, J., Chen, Y., Qin, C., Luo, F., & Guerrero, J. M. (2026) · Applied Energy · doi:10.1016/j.apenergy.2026.127625 · Cited by 35 · see citing works

EnergyPlus-MCP: A model-context-protocol server for ai-driven building energy modeling

Han, L., Xu, Y., & Hong, T. (2025) · SoftwareX · doi:10.1016/j.softx.2025.102367 · Cited by 32 · see citing works

Exploring Gen-AI applications in building research and industry: A review

Wan, H., Zhang, J., Chen, Y., Xu, W., & Feng, F. (2025) · Building Simulation · doi:10.1007/s12273-025-1279-x · Cited by 37 · see citing works

A systematic review of building energy performance forecasting approaches

Yang, Y., Duan, Q., & Samadi, F. (2025) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2025.116061 · Cited by 29 · see citing works

Ontology-assisted GPT-based building performance simulation and assessment: Implementation of multizone airflow simulation

Song, J., & Yoon, S. (2024) · Energy and Buildings · doi:10.1016/j.enbuild.2024.114983 · Cited by 28 · see citing works

A Comprehensive Survey of Retrieval-Augmented Large Language Models for Decision Making in Agriculture: Unsolved Problems and Research Opportunities

Vizniuk, A., Diachenko, G., Laktionov, І., Siwocha, A., Xiao, M., & Smoląg, J. (2025) · Journal of Artificial Intelligence and Soft Computing Research · doi:10.2478/jaiscr-2025-0007 · Cited by 32 · see citing works

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

Lu, J., Zheng, Z., Langtry, M., Jackson, M., Zhao, Y., Feng, C., Zhang, R., Zhang, C., Zhang, J., & Choudhary, R. (2025) · iScience · doi:10.1016/j.isci.2025.113867 · Cited by 28 · see citing works

Virtual sample diffusion generation method guided by large language model-generated knowledge for enhancing information completeness and zero-shot fault diagnosis in building thermal systems

Sun, Z., Yao, Q., Shi, L., Jin, H., Xu, Y., Yang, P., Han, X., Chen, D., Zhao, P., & Shen, X. (2025) · Journal of Zhejiang University. Science A · doi:10.1631/jzus.a2400560 · Cited by 27 · see citing works

Generative AI for Sustainable Smart Environments: A Review of Energy Systems, Buildings, and User-Centric Decision-Making

Vamvakas, D. G., Papaioannou, I. V., Tsaknakis, C. D., Sgouros, T., & Korkas, C. D. (2025) · Energies · doi:10.3390/en18236163 · Cited by 30 · see citing works

Ten questions concerning calibrating building energy simulation models

Chong, A., Yan, D., Sun, K., Zhan, S., Cheng, S., Wu, Y., Chen, Y., & Hong, T. (2025) · Building and Environment · doi:10.1016/j.buildenv.2025.113404 · Cited by 24 · see citing works

AI BIM coordinator for non-expert interaction in building design using LLM-driven multi-agent systems

Dong, Y., Zhan, Z., Hu, Y., Doe, D. M., & Han, Z. (2025) · Automation in Construction · doi:10.1016/j.autcon.2025.106563 · Cited by 24 · see citing works

Metadata schema for virtual building models in digital twins: VB schema implemented in GPT-based applications

Lee, J., & Yoon, S. (2024) · Energy and Buildings · doi:10.1016/j.enbuild.2024.115039 · Cited by 23 · see citing works

Can large language models replace human experts? Effectiveness and limitations in building energy retrofit challenges assessment

Chen, L., Darko, A., Zhang, F., Chan, A. P. C., & Yang, Q. (2025) · Building and Environment · doi:10.1016/j.buildenv.2025.112891 · Cited by 22 · see citing works

AI for building energy modeling: A transformation

Hong, T., & Zhang, L. (2025) · Building Simulation · doi:10.1007/s12273-025-1329-4 · Cited by 21 · see citing works

Agentic Built Environments: a review

Lee, J., Song, J., Koo, J., Choi, S., Hwang, J., Saif, S. M. H., Li, Y., Li, J., Yoo, J., Lee, G., et al. (2025) · Energy and Buildings · doi:10.1016/j.enbuild.2025.116159 · Cited by 21 · see citing works

AI Large Models for Power System: A Survey and Outlook

Yao, Q. J., Fang, F., Chen, Y., Liu, J., Mo, H., & Ao, Y. (2025) · IET Smart Energy Systems · doi:10.1049/ses2.70000 · Cited by 20 · see citing works

Large language models in building energy applications: a survey

Arslan, M., & Munawar, S. (2025) · Energy and Buildings · doi:10.1016/j.enbuild.2025.116800 · Cited by 19 · see citing works

Customized large-scale model for human-AI collaborative operation and maintenance management of building energy systems

Chen, S., Liang, X., Ying, L., Li, X.-L., Jin, X., & Du, Z. (2025) · Applied Energy · doi:10.1016/j.apenergy.2025.126169 · Cited by 18 · see citing works

User-friendly AI-driven automation for rapid building energy model generation

ElSayed, M., Shultz, J., & Kurtz, J. (2025) · Energy and Buildings · doi:10.1016/j.enbuild.2025.116092 · Cited by 18 · see citing works

Ten questions on building stock modeling to inform energy efficiency and sustainability

Hong, T., Malik, J., Langevin, J., Liu, Z., & Yan, D. (2025) · Building and Environment · doi:10.1016/j.buildenv.2025.113465 · Cited by 18 · see citing works

Neural ordinary differential equations-based approach for enhanced building energy modeling on small datasets

Ma, Z., Jiang, G. Y., & Chen, J. (2025) · Building Simulation · doi:10.1007/s12273-025-1269-z · Cited by 15 · see citing works

From design to operation: Multi-agent AI for virtual in-situ modeling of digital twins in BIM

Lee, J., Li, J., & Yoon, S. (2025) · Automation in Construction · doi:10.1016/j.autcon.2025.106477 · Cited by 14 · see citing works

Ten questions concerning Large Language Models (LLMs) for building applications

Ma, N., Labib, R., Amor, R., Chong, A., Fan, C., Forth, K., Fu, X., Fuchs, S., Hong, T., Klimenkova, N., et al. (2026) · Building and Environment · doi:10.1016/j.buildenv.2026.114260 · Cited by 15 · see citing works

Public sentiment analysis of data center energy consumption using social media data and large language models

Wang, H., Hua, W., Peng, J., & Hu, M. (2025) · Energy and Buildings · doi:10.1016/j.enbuild.2025.115802 · Cited by 13 · see citing works

Large language models in smart construction: a systematic review of implementation strategies, applications and future directions

Yu, G., Yiu, T. W., Shen, X., & Tam, V. W. Y. (2026) · Engineering Construction & Architectural Management · doi:10.1108/ecam-10-2025-1668 · Cited by 15 · see citing works

Smart Building Recommendations with LLMs: A Semantic Comparison Approach

Papaioannou, I., Korkas, C. D., & Kosmatopoulos, E. B. (2025) · Buildings · doi:10.3390/buildings15132303 · Cited by 13 · see citing works

Leveraging reinforcement learning for optimal control of chiller plant with complex hydraulic and thermodynamic characteristics

Wang, X., Xu, H., Fan, C., Tan, S., Kang, X., Shi, Y., & Yan, D. (2025) · Energy and Buildings · doi:10.1016/j.enbuild.2025.116037 · Cited by 12 · see citing works

Enhancing LLM-based building data query with chain-of-thought, retrieval-augmented generation, and fine-tuning

Li, M., Hu, Z., Mohebi, P., Li, S., & Wang, Z. (2025) · Automation in Construction · doi:10.1016/j.autcon.2025.106738 · Cited by 16 · see citing works

Large language models and AI agents in disaster-resilient infrastructure: concepts, applications, pathways, and challenges

Cui, J., Zhai, C., Wang, Y., & Li, Y. (2026) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2026.112497 · Cited by 12 · see citing works

Spatiotemporal dynamics panorama of operational energy consumption and carbon emissions in bamboo buildings: A sample of 275 cities in China

Xu, P., Li, Z., Xu, X., & Mao, P. (2025) · Energy · doi:10.1016/j.energy.2025.136165 · Cited by 12 · see citing works

LLM-enabled multi-agent framework for natural language interaction with graph-based digital twins

Pan, Y., Wang, M., Lu, L., Lamsal, R., Parn, E. A., Sisi Zlatanova, B., & Brilakis, I. (2026) · Automation in Construction · doi:10.1016/j.autcon.2026.106791 · Cited by 11 · see citing works

Configuration and parameter Design of electrified propulsion systems for three-dimensional Transportation: A comprehensive review

Zou, Y., Yang, Y., & Zhang, Y. (2025) · Green Energy and Intelligent Transportation · doi:10.1016/j.geits.2025.100286 · Cited by 11 · see citing works

Integrating large language models, reinforcement learning, and machine learning for intelligent indoor thermal comfort regulation

Liu, D., Ling, F., Zhou, X., & Li, Y. (2025) · Architectural Science Review · doi:10.1080/00038628.2025.2488522 · Cited by 12 · see citing works

Machine learning-driven processes in architectural building design

Lystbæk, M. S. (2025) · Automation in Construction · doi:10.1016/j.autcon.2025.106379 · Cited by 11 · see citing works

AECBench: A hierarchical benchmark for knowledge evaluation of large language models in the AEC field

Chen, L., Huang, Z., Wang, H., Chai, F., Yu, C., Wei, H., Liu, Z., Li, Y., Wang, H., Luo, R., et al. (2026) · Advanced Engineering Informatics · doi:10.1016/j.aei.2026.104314 · Cited by 12 · see citing works

Development of a dynamic multi-agent network for building energy modeling: A case study towards scalable and autonomous energy modeling

Xu, W., Wan, H., Goel, S., & Antonopoulos, C. (2025) · Energy and Buildings · doi:10.1016/j.enbuild.2025.116712 · Cited by 12 · see citing works

Large Language Model for Secure Operation of Power Systems

Xiang, Y., Tan, L., Qiu, G., Tang, Z., & Liu, J. (2025) · Smart Energy System Research · doi:10.70322/sesr.2025.10005 · Cited by 9 · see citing works

AutoControl: An end-to-end fully automated workflow for control design of building energy systems

Hu, Z., Li, M., Tang, H., & Wang, Z. (2025) · Energy · doi:10.1016/j.energy.2025.138329 · Cited by 9 · see citing works

Agentic fault detection and diagnosis for building HVAC systems using AI agent, ontology, and diagnosis rules

Li, J., Koo, J., Lee, J., Wang, P., Zhao, T., & Yoon, S. (2025) · Building and Environment · doi:10.1016/j.buildenv.2025.114104 · Cited by 8 · see citing works

A review of smart integrated energy systems towards industrial carbon neutrality: Opportunity and challenge

Mao, Y., Zhang, N., Shi, P., Zhong, W., & Lin, X. (2026) · npj Thermal Science and Engineering · doi:10.1038/s44435-025-00003-0 · Cited by 9 · see citing works

Large Language Model for Low-Carbon Energy Transition: Roles and Challenges

Cheng, Y., Zhou, X., Zhao, H. Q., Gu, J., Wang, X., & Zhao, J. (2024) · Power System and Green Energy Conference (PSGEC) · doi:10.1109/psgec62376.2024.10721191 · Cited by 7 · see citing works

Multi-condition building decarbonization using deep reinforcement learning and large language model

Zhang, L., Huang, J., & Chen, C. (2025) · Energy and Buildings · doi:10.1016/j.enbuild.2025.115810 · Cited by 7 · see citing works

EnergiQ: A Prescriptive Large Language Model-Driven Intelligent Platform for Interpreting Appliance Energy Consumption Patterns

Papaioannou, C., Tzitzios, I., Papaioannou, A., Dimara, A., Anagnostopoulos, C., & Krinidis, S. (2025) · Sensors · doi:10.3390/s25164911 · Cited by 7 · see citing works

Unleashing the Potential of Large Language Models in Urban Data Analytics: A Review of Emerging Innovations and Future Research

Jiang, F., Ma, J., & Jin, Y. (2025) · Smart Cities · doi:10.3390/smartcities8060201 · Cited by 7 · see citing works

Artificial intelligence in five key phases of architectural heritage conservation: A systematic review

Tan, X., Xie, H., Xia, C., Yuan, Y., Hokoi, S., & Li, Y. (2025) · Journal of Building Engineering · doi:10.1016/j.jobe.2025.114862 · Cited by 7 · see citing works

Sketch-to-Energy model automation with multimodal LLMs and retrieval-augmented generation

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Microclimate-integrated urban building energy modeling (MUBEM): Spatiotemporal analysis of climate-driven energy use in Shanghai and Singapore

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Ly, R., Shojaei, A., & Gao, X. (2025) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3696630.3728706 · Cited by 6 · see citing works

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PV-GPT: query the PV data using natural language

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LLM-Driven Agent-Based Evaluation Framework for Occupant-Centric HVAC Control Considering Individual Differences and Movement Behavior

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AutoLFM: A multi-agent LLM framework for automated building load forecasting

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AI-Enabled Digital Twins in the Built Environment: A Bibliometric Review of Applications, Trends, and Future Directions

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Semi-automated reconstruction and editing of building energy models from vector floor plans

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A review of Carnot battery and its role towards smart integrated energy systems: Opportunities and challenges

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Large Language Models for Building Energy Retrofit Decision-Making: Technical and Sociotechnical Evaluations

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Pre-training large language models based on Transformer architecture for building industry application: A review

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AutoBEE: A hierarchical multi-agent approach for energy and environmental parameter analysis

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Automatic synthesis of econometric empirical research results using large language model: A case study of digitalization-greening relationships

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Cost-optimal residential energy scheduling via a zero-shot LLM agent and model predictive control

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Large language model–driven building energy model generation

Liu, D., Zhou, X., & Li, Y. (2026) · Energy and Buildings · doi:10.1016/j.enbuild.2026.117255 · Cited by 4 · see citing works

When control meets large language models: From words to dynamics

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Comprehensive survey of large models-driving intelligent decision making

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Ef-Llm: Energy Forecasting Llm with Ai-Assisted Automation, Enhanced Sparse Prediction, Hallucination DetectionPreprint

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Statistical and Machine Learning Approaches for Electrical Energy Forecasting

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Thermal pyrolysis optimization and production forecasting in heterogeneous shale formations for enhanced oil recovery with large language models

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Parsing and prioritizing users’ evolving requirements with large language models: A case study in personalized rehabilitation assistive devices design

Zhuang, W., Wang, J., Wu, X., Chen, C., Xiao, W., Jiang, C., Yao, J., Bao, J., & Li, X. (2026) · Computers & Industrial Engineering · doi:10.1016/j.cie.2026.111878 · Cited by 3 · see citing works

Rethinking keys in data-driven short-term building energy predictions: Towards standardized methods for data preparation, model training and evaluation

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Leveraging Large Language Models for Discrepancy Value Prediction in Custody Transfer Systems: A Comparative Analysis of Probabilistic and Point Forecasting Approaches

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AI-based personal conditioning with physiological awareness: Fine-tuned small and large language models vs. neural networks

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Co-LLM: A retrieval-augmented language model framework for chiller optimization control

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Leveraging LLM for P&ID-based Automated Code Generation in HVAC Fault Detection and Diagnosis

Lu, C., & Itard, L. (2026) · Lecture notes in civil engineering · doi:10.1007/978-3-032-10546-2_37 · Cited by 2 · see citing works

A knowledge-driven and interpretable fine-tuning LLMs framework for energy retrofit decision support in commercial buildings

Tang, Huang, Z., Liao, Xu, X., & Li, N. (2026) · Energy and Buildings · doi:10.1016/j.enbuild.2026.117486 · Cited by 2 · see citing works

Efficient fine-tuning of large language models for automated building energy modeling in complex cases

Jiang, G., & Chen, J. (2025) · Automation in Construction · doi:10.1016/j.autcon.2025.106223 · Cited by 44 · see citing works

Large language model–driven building energy model generation

Liu, D., Zhou, X., & Li, Y. (2026) · Energy and Buildings · doi:10.1016/j.enbuild.2026.117255 · Cited by 4 · see citing works

Automatic building energy model development and debugging using large language models agentic workflow

Zhang, L., Ford, V., Chen, Z., & Chen, J. (2024) · Energy and Buildings · doi:10.1016/j.enbuild.2024.115116 · Cited by 79 · see citing works

Prompt engineering to inform large language model in automated building energy modeling

Jiang, G., Ma, Z., Zhang, L., & Chen, J. (2025) · Energy · doi:10.1016/j.energy.2025.134548 · Cited by 63 · see citing works

A large language model-based framework with retrieval-augmented generation for automated building energy modeling

Zhang, Y., & Xu, P. (2026) · Springer Link (Chiba Institute of Technology) · doi:10.1051/e3sconf/202668908004/pdf