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

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

Attention Is All You Need

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

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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

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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

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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

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

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

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 111

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 101

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 86

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

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 110

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 79

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 67

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

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 64

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 53

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 94

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 50

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 45

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 46

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

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 48

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

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

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

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

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 42

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

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 30

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 36

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

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

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

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

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 34

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 24

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 24

AI for building energy modeling: A transformation

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

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 23

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

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

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 23

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 23

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

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 16

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

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 17

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

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 18

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

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

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

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

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

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 14

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 12

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 13

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

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 15

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

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 10

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

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

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 10

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 8

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

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

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 8

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

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

Zhang, Y., Wang, G., & Xu, P. (2026) · Energy and Buildings · doi:10.1016/j.enbuild.2026.117387 · Cited by 7

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

Jie, P., Tian, S., Liu, P., Wang, J., Santamouris, M., Hu, M., & Shi, X. (2026) · Building and Environment · doi:10.1016/j.buildenv.2026.114749 · Cited by 7

Smart Building Operations and Virtual Assistants Using LLM

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

Energy-efficient urban retrofits benefits from a novel demand-supply network with rapid urban building energy model and network analysis

Su, F., Zhu, Y., Pan, J., Luo, L., Jing, R., Hu, Q., Wang, W., & Sun, Y. (2025) · Building Simulation · doi:10.1007/s12273-025-1326-7 · Cited by 6

Physics-informed neural network-based model predictive control for chiller plant – fan coil unit system and intelligent human-AI interaction via large language model

Liang, X., Chen, S., Mao, Z., Li, X.-L., Jin, X., & Du, Z. (2025) · Energy · doi:10.1016/j.energy.2025.139149 · Cited by 6

TEF-PLM: A Tabular and Embeddings Fusion Framework using Pretrained Language Model for enhanced electric vehicle energy forecasting

Waqar, M., Kim, Y.-W., & Byun, Y.-C. (2025) · Energy Reports · doi:10.1016/j.egyr.2025.11.004 · Cited by 6

Socio-technical assessment of generative AI integration in architecture, engineering, and construction (AEC) workflows: An empirical study using O*NET occupational taxonomy

Xiong, R., Netser, Y., Tang, P., Li, B., & Hwang, J. (2026) · Advanced Engineering Informatics · doi:10.1016/j.aei.2026.104392 · Cited by 10

An agentic AI-enabled physics-informed machine learning framework for grid-interactive, decarbonized building operations

Jiang, Z., Xu, W., & Dong, B. (2026) · Advances in Applied Energy · doi:10.1016/j.adapen.2026.100273 · Cited by 6

Computational design of indoor lighting supported by artificial intelligence: Recent advances and future prospects

Chen, P., Wang, L., Wu, Y., Liang, Z., Yu, J., & Chen, T. (2025) · Building and Environment · doi:10.1016/j.buildenv.2025.113575 · Cited by 10

PV-GPT: query the PV data using natural language

Zhang, D., Li, M., & Wang, Z. (2025) · Journal of Building Engineering · doi:10.1016/j.jobe.2025.114535 · Cited by 5

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

Sasaki, S., Techasarntikul, N., Ohsita, Y., & Shimonishi, H. (2025) · ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys) · doi:10.1145/3736425.3772325 · Cited by 5

Benchmarking Knowledge and Capability of Large Language Models in Building Science Domain

Jiang, G., Chen, K., Liu, J., Chen, J.-L., Zhe, S.-D., & Wang, Z. (2025) · Energy Use · doi:10.59717/ipj.energy-use.2025.100026 · Cited by 4

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

Li, S., Zheng, W., Li, M., Xing, S., & Wang, Z. (2026) · Building Simulation · doi:10.1007/s12273-025-1385-7 · Cited by 5

NPP-GPT: Forecasting nuclear power plants operating parameters using pre-trained large language model

Chang, L., Yu, H.-B., Yang, M., Zhang, Z., Chen, S., & Wang, J. (2026) · Applied Energy · doi:10.1016/j.apenergy.2026.127438 · Cited by 5

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

Wang, Y., Schnabel, M. A., Zhang, Y.-J., Wang, K., & Guo, F.-Y. (2026) · Buildings · doi:10.3390/buildings16040809 · Cited by 5

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

Liao, X., Li, B., Li, N., Du, C., Zhang, S., Li, G., & Tang, C. (2026) · Automation in Construction · doi:10.1016/j.autcon.2026.106882 · Cited by 5

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

Guo, W., Jin, X., Jiang, H., Xia, Y., Zhu, J., Qu, B., Zhong, W., Jiang, L., & Lin, X. (2026) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2026.116928 · Cited by 5

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

Shu, L., Yeganeh, A. J., & Zhao, D. F. (2025) · Buildings · doi:10.3390/buildings15224081 · Cited by 4

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

Liang, J., Lin, Y., Liu, Y., Huang, Y., & Wu, H. (2025) · Building Simulation · doi:10.1007/s12273-025-1324-9 · Cited by 7

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

Quan, Y., Xiao, T., Gu, J., & Xu, P. (2025) · Energy and Buildings · doi:10.1016/j.enbuild.2025.116516 · Cited by 4

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

Guo, Z., Yang, G., & Wu, W. (2025) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2025.124370 · Cited by 6

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

Raghavan, V. S., & Vijayaraghavulu, G. A. (2025) · Journal of Building Performance Simulation · doi:10.1080/19401493.2025.2605465 · Cited by 4

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

When control meets large language models: From words to dynamics

Nosrati, K., Tepljakov, A., Belikov, J., & Petlenkov, E. (2026) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2026.115119 · Cited by 8

Comprehensive survey of large models-driving intelligent decision making

Zheng, Y., Wu, T., Xiao, X., & Xu, Z. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.113524 · Cited by 5

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

Qiu, Z., Ravishankar, J., Wang, Z., & Xie, R. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5217709 · Cited by 4

Statistical and Machine Learning Approaches for Electrical Energy Forecasting

Machado, S., & Xingquan Zhu, A. (2025) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.70033 · Cited by 3

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

Chen, B., Gao, K., Sun, H., Zhou, C., & Yang, H. (2025) · International Communications in Heat and Mass Transfer · doi:10.1016/j.icheatmasstransfer.2025.109844 · Cited by 3

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

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