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

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

Energy-efficient HVAC control in residential buildings through occupant intention inference with multimodal large language models

Qi, Z., Yang, Z., Liu, M., & O’Neill, Z. D. (2026) · Advances in Applied Energy · doi:10.1016/j.adapen.2026.100283 · Cited by 3

AI for science: Progress, challenges, and perspectives

Xu, Y., Shao, Z., Liu, X., Li, Y., An, Z., Fu, C., Huang, C., Zhang, L., Gao, F., Li, J., et al. (2026) · The Innovation · doi:10.1016/j.xinn.2026.101530 · Cited by 6

Integration of Artificial Intelligence for the Sustainable Optimization of Photovoltaic Systems: A Comprehensive Review

Bouaichi, A., Azouzoute, A., Chahet, Y., Laarabi, B., Zitouni, H., Ydrissi, M. E., Bounoua, Z., Hajjaj, C., Amrani, A. E., Amraoui, M. E., et al. (2026) · Sustainability · doi:10.3390/su18168124 · Cited by 2

Transforming Architecture, Engineering, and Construction (AEC) Workflows with Generative AI: Opportunities, Risks, and Future DirectionsPreprint

Xiong, R., Netser, Y., Tang, P., Li, B., & Hwang, J. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5168581 · Cited by 2

Unveiling spatial and temporal variations of building energy use and anthropogenic heat emissions in Hong Kong

Liu, Q., & Zhou, Y. (2026) · Energy and Buildings · doi:10.1016/j.enbuild.2026.117663 · Cited by 1

AI agent-driven virtual in-situ calibration for intelligent building digital twins

Li, J., Koo, J., Lee, J., Wang, P., Zhao, T., & Yoon, S. (2025) · Energy · doi:10.1016/j.energy.2025.138968 · Cited by 1

Spectroscopy and Artificial Intelligence-driven coal quality detection: a review

Zhou, S., Bai, X., Li, S., Cui, J., Wu, J., Huang, G., Deng, J., Cao, Y., Huangfu, Z., Li, J., et al. (2026) · International Journal of Coal Preparation and Utilization · doi:10.1080/19392699.2026.2616231 · Cited by 1

SolarFM: Spatio-temporal solar irradiance forecasting based on time series foundation model

Shan, S., Dou, W., Sreeram, V., Li, C., Wang, K., Zhang, K., & Wei, H. (2026) · Applied Energy · doi:10.1016/j.apenergy.2026.127532 · Cited by 1

Human-in-the-Loop: Integrating Virtual occupant behavior simulation and semantic Decision-Making for building energy efficiency

Liu, D., Zhou, X., & Li, Y. (2026) · Energy and Buildings · doi:10.1016/j.enbuild.2026.117206 · Cited by 2

Tracing the sources of online prediction errors in building energy consumption: A bias-variance and time-frequency domain based approach

Fang, H., Yuan, X., Liu, Z., Guo, S., & Zhai, X. (2026) · Energy · doi:10.1016/j.energy.2026.140566 · Cited by 1

A large-language-model-guided progressive error-correction framework for structured process modeling

Xu, R., Fang, H., Fang, W., Wang, F., & Zhao, C. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.131998 · Cited by 1

Control strategies for district heating and cooling systems: a comprehensive review across production, distribution, and end-user levels

Monghasemi, N., Vadiee, A., Vouros, S., & Kyprianidis, K. (2026) · Energy Informatics · doi:10.1186/s42162-026-00650-9 · Cited by 1

LLM-powered agent for occupant-centric indoor environment control: Design, implementation, and evaluation

Lyu, J., Lai, D., Zhang, H., & Lian, Z. (2026) · Energy and Built Environment · doi:10.1016/j.enbenv.2026.04.001 · Cited by 1

LLM-Driven Framework for Industrial Design Automation

He, S., Wang, X., Chen, Z., Liao, J., Wang, B., Xu, J., Guan, X., Yu, S., & Li, Y. (2026) · Engineering · doi:10.1016/j.eng.2026.04.009 · Cited by 1

A knowledge-based multi-agent system using large language models for automated building energy modeling and simulation service

Lee, G., & Yoon, S. (2026) · Energy and Buildings · doi:10.1016/j.enbuild.2026.117688 · Cited by 1

Agentic AI-Enabled Framework for Thermal Comfort and Building Energy Assessment in Tropical Urban Neighborhoods

Lai, P.-Y., Yang, X., Low, D., Liu, H., & Wong, J. C. (2026) · E3S Web of Conferences · doi:10.1051/e3sconf/202671606004 · Cited by 2

Achieving high-precision automated urban block units modeling through domain enhancement and instruction fine-tuning of large language models

Liu, D., Zhou, X., & Li, Y. (2026) · Energy and Buildings · doi:10.1016/j.enbuild.2026.117816 · Cited by 1

AgentiControl-MPC: Automated model predictive control for buildings using large language model-based multi-agent system

Ren, C., Hong, S. M., & Tang, R. (2026) · Advanced Engineering Informatics · doi:10.1016/j.aei.2026.105062 · Cited by 1

Whole-process AI in building applications: A framework of integrating AI with domain knowledge

Yan, D., Dong, B., O’Neill, Z. D., Chen, Z., Wang, X., Jiang, Z., Song, R., Kang, X., & Zhou, X. (2026) · Building Simulation · doi:10.1007/s12273-026-1487-z · Cited by 1

Grounding agentic AI in building semantics: From information retrieval to automated simulation

Peng, Z., Chen, Y., Timoudas, T. O., & Wang, Q. (2026) · Energy and Buildings · doi:10.1016/j.enbuild.2026.118187 · Cited by 1

Tokenizing battery degradation: a physics-informed and uncertainty-aware framework for state-of-health estimation

Huang, S., Jiao, J., Li, H., & Jing, L. (2026) · Applied Energy · doi:10.1016/j.apenergy.2026.129000 · Cited by 1

Künstliche Intelligenz in der Bauphysik – Hintergrund, Anwendungen und Potenziale

Kraus, M. A., Waibel, C., Bishara, N., & Griego, D. (2025) · doi:10.1002/9783433612095.ch11

A cognitive domain specific framework integrating large language model for COVID-19 vaccine sentiment analysis

Prasika, L., & Rajan, S. (2025) · Intelligent Data Analysis · doi:10.1177/1088467x251348350

Pioneering Academic Innovation

Bircan, A., Mandal, D., & Kösesoy, İ. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-1414-3.ch004

Poster Abstract: Text-To-EnergyPlus: Translating Natural Language into Building Energy Simulation

Zhao, K., Dieng, O., & Lee, S. (2025) · ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys) · doi:10.1145/3736425.3772120

LIEC-FT: A Large-Language-Model-Guided Iterative Error-Correction Framework for Small-Model Generation of Procedural GraphsPreprint

Ruilong, X., Fang, H., Fang, W., Wang, F., & Zhao, C. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5993790

IoT and LLM Supported Digital Twin Platform: A Case Study on a Net Zero Oriented Building

Li, J., Hu, X., & Shen, J. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-5015-9_28

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

Zhang, Y., & Xu, P. (2026) · E3S Web of Conferences · doi:10.1051/e3sconf/202668908004

AutoCoSim: An LLM-RAG-based Multi-agent Framework for Automating Building Energy and CFD Co-simulationPreprint

Li, Y., Huang, Y., Chen, X., & Chen, B. M. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6172101

Microclimate-Integrated Urban Building Energy Modeling (MUBEM): Spatiotemporal Analysis of Climate-Driven Energy Use — Comparative Studies of Shanghai and SingaporePreprint

Jie, P., Tian, S., Liu, P., Wang, J., Santamouris, M., Hu, M., & Shi, X. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6203923

Closing the Expertise Gap in Residential Building Energy Retrofits: A Domain-Specific LLM for Informed Decision-MakingPreprint

Shu, L., Yeganeh, A. J., Mollaoglu, S., Zhou, J., & Zhao, D. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6210302

OptAgent: An Agentic AI framework for Intelligent Building Operations based on a Physics-Informed Simulation EnvironmentPreprint

Jiang, Z., Xu, W., & Dong, B. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6353019

Prompt Engineering in Large Language Models Improves Usability but Not Time and Effectiveness for Building Energy ModelingPreprint

Sur, D. A., & Jain, R. K. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6603315

A knowledge-intensive LLM-assisted evolutionary framework for multi-objective geometric design of high-performance concrete structures

Lu, Z., Wang, H., Song, J., Tian, X., & Lin, A. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.132589 · Cited by 1

How Buildings Are Textualized for Large Language Models Processing: A Preliminary Study

Peng, Z., Timoudas, T. O., & Wang, Q. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-19137-3_15

Self-powered intelligent sensing networks for next-generation super airports

Yang, H., Kong, L., Fang, Z., Tang, H., Zhao, X., Wu, X., & Zhang, Z. (2026) · Sustainable Energy Technologies and Assessments · doi:10.1016/j.seta.2026.105078

Servo motor forecasting in EV battery swapping systems via time-frequency and human-in-the-loop memory enhancement

Zhang, Q., Li, X., Wen, X., Bao, J., Zhang, D., & Xu, Z. (2026) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2026.05.007

Early-stage shading design evaluation for buildings: A workflow accessing energy performance on AI-generated 3D geometry

Graça, A. S., & Zhang, X. (2026) · E3S Web of Conferences · doi:10.1051/e3sconf/202671602042

A Comparative Analysis of Fine-Tuning and Prompt Engineering for LLM-Based Automation of Building Energy Models for Rooftop Photovoltaics

Pagri, M. I. F., Lee, D., Hwang, J., & Suh, D. (2026) · E3S Web of Conferences · doi:10.1051/e3sconf/202671606011

A Knowledge-Enhanced Large Language Model Advisor for Degradation-Aware Microgrid Battery Dispatch Under Extreme Heat

Shen, S., Yi, L., Wang, J., Duan, J., & Chen, Y. (2026) · International Conference on Energy, Electrical and Power Engineering (CEEPE) · doi:10.1109/ceepe69795.2026.11552122

JARVIS for HVAC: LLM-based Question-Answer Framework for Sensor-driven HVAC System Interaction

Lee, S., Lee, J., Kang, M., Lee, S., Kim, D., Hong, J., Shin, J., Zhang, P., & Ko, J. (2026) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3810210 · Cited by 8

Intelligent mining of generation operation rules for cascade hydropower reservoirs driven by large language models and knowledge graphs

Feng, Z.-K., Lin, T., Niu, W., Xiao, Y., Yang, T., & Tang, H. (2026) · Journal of Hydrology · doi:10.1016/j.jhydrol.2026.135896

Semantic error debugging of building energy models: a multi-agent approach based on retrieval-augmented generation

Yun, W.-S., & Seo, H. (2026) · Energy and Buildings · doi:10.1016/j.enbuild.2026.117786

Knowledge-Guided Self-Reflection LLM Agents for HVAC Simulation in EnergyPlus

Dieng, O. T., Zhao, K., & Lee, S. (2026) · ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys) · doi:10.1145/3744256.3812593

Benchmarking Data-Driven Time Series Classification Methods for a real-world Building HVAC System

Hadwiger, M., Hirsch, T., Masoumali, S. K., & Schweiger, G. (2026) · ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys) · doi:10.1145/3744256.3812577

Beyond Prototypes: Overcoming the UBEM Data Bottleneck with Large Multimodal Models

Dubois, R., & Howard, B. (2026) · ACM Sustainability Week · doi:10.1145/3765611.3815360

Research on Large Language Models for Building EnergyConsumption Integrating Domain-Specific Fine-Tuning andMulti-Agent Retrieval-Augmented GenerationPreprint

Wang, P., Yan, H., & Sun, Y. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6997375

From surrogate modelling to AI: how machine learning is transforming building simulation

Evins, R. (2026) · Journal of Building Performance Simulation · doi:10.1080/19401493.2026.2694582

Large language models for transportation energy integration: A functional framework and future directions

Chen, Y., Ou, S. S., Lv, Z., Yang, Z., Gu, L., & Ma, W. (2026) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2026.117243

Integrating domain-specific knowledge graph with large language model for question-answering of construction laws and regulations: The case of China

Zhou, S., Wang, J., Brilakis, I., Li, D., Wei, R., Ng, S. T., Ji, Y., & Zhou, Y. (2026) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2026.115514

A Gaussian Process-based method for interactive individual occupant comfort modelingPreprint

Shen, G., Malkawi, A. M., & Li, N. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7050861

OpenStudio-MCP: A skill-augmented MCP server for AI-driven building energy modeling using OpenStudio SDKPreprint

Ball, B. L., Long, N. L., Fleming, K., & Goldwasser, D. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7067741

A Literature Review on the Application of Physical AI Robotics for Zero-Energy Building Performance Gap

Lee, J. (2026) · Journal of the Korean Solar Energy Society · doi:10.7836/kses.2026.46.3.109

Generative Artificial Intelligence in Geography: Structure and Evolution of an Emerging Field

Ng, S. L., & Chu, C.-M. (2026) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi15070312

Real-time thermal comfort monitoring guided by public participation: An adaptive robot system

Lin, C.-Y., & Zhang, Y. (2026) · Building and Environment · doi:10.1016/j.buildenv.2026.115022

Explainable Risk Assessment and Intelligent Maintenance for Electrochemical Energy Storage System with Large Language ModelPreprint

Wang, Y., Fu, Y., Lu, Y., Qu, J., Zhang, P., & Li, M. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7152490

PipeClaw: An operation support system for natural gas pipelines with operation history, transient prediction, and verified dispatch

Zhang, L., Huang, D., Yu, W., Zhu, H., Yang, W., Feng, D., Xie, H., & Zhang, H. (2026) · Journal of Pipeline Science and Engineering · doi:10.1016/j.jpse.2026.100572

A multimodal information transformation framework for language-driven scene reconfiguration in construction automation

Guo, Y., Zhang, W., Zhang, Y., Gao, W., Sun, X., Liu, J., & Jia, X. (2026) · Advanced Engineering Informatics · doi:10.1016/j.aei.2026.105075

From systematic evidence to agentic architectures: A systematic review and framework for large language model–driven smart grids

Rezaei, Z., Amini, M. A., & Banad, Y. M. (2026) · Energy and AI · doi:10.1016/j.egyai.2026.100853

Explainable Risk Assessment and Intelligent Maintenance for Electrochemical Energy Storage System with Large Language ModelPreprint

Wang, Y., Fu, Y., Lu, Y., Qu, J., Zhang, P., & Li, M. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7195888

Towards Automated Generation of Diagnostic Bayesian Networks for HVAC Systems Using Large Language Models: A Feasibility StudyPreprint

Lu, C., Walker, S. S. W., Chen, Y., Itard, L., & Saelens, D. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7211771

Dialectical systems framework for LLM applications in intelligent construction and operation

Xu, X., & Li, Z. (2026) · Automation in Construction · doi:10.1016/j.autcon.2026.107186

Integrating Large Language Models and Reinforcement Learning for Efficient Home Energy Management

Zhang, Y., Wang, C., Xie, L., Xu, Y., Chung, E., Shahidehpour, M., & Wen, F. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3719299

A Multi-Agent Digital Twin Framework for LLM-Driven Building Energy and Indoor Air Quality Co-Simulation

Huang, Y., Li, Y., Chen, X., & Chen, B. M. (2026) · International Conference on Control and Automation · doi:10.1109/icca69928.2026.11618077

A scenario-based digital-twin assessment of vision–language models for building energy demand forecasting and automated asset intelligence

Palivela, H., & Arif, A. (2026) · Sustainable Energy Technologies and Assessments · doi:10.1016/j.seta.2026.105238

Addressing data sparsity in thermal comfort evaluation: A dynamic constraint relaxation framework using large language models

Wang, Z., Feng, F., & Pang, Z. (2026) · Building and Environment · doi:10.1016/j.buildenv.2026.115106

Integrating information flow-knowledge graphs and fine-tuning LLM for knowledge-guided process engineering in domain-specific task

Liao, X., Li, B., Broyd, T., Han, S., Tang, C., & Li, N. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2026.116852

A decision-time validation framework for LLM-assisted energy studies: One-hour upward-reserve sizingPreprint

Zheng, Y., Liu, Y., & Zhong, H. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7275100

A Generative AI-Based Virtual Consultant for Residential Building Energy Modelling and Retrofit Decision Support

Saffari, M., Ivare, T., Bampoulas, A., & Mangina, E. (2026) · International Conference on Computer, Information and Telecommunication Systems · doi:10.1109/cits70307.2026.11637238

Adaptive Weather-Aware Home Energy Management through Large Language Model-Based Appliance SchedulingPreprint

Jonah, S., Moses, Q., Babatunde, A., Ajao-Olarinoye, M., & Bammeke, D. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7289632

Edge language models and agentic AI in building automation: a scoping review of evidence maturity and authority tiersPreprint

Simson, R., Kiil, M., Võsa, K.-V., & Kurnitski, J. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7300707

From Single Buildings to Clusters: A Pre-Trained Large Language Model-Based Framework for Cross-Building and Data-Scarce Energy Consumption Forecasting

Wang, C., Li, S., Arıcı, M., Yang, R., Xu, X., Wang, Z., Sina, A., & Liu, S. (2026) · Buildings · doi:10.3390/buildings16163281

Large language models for human-centric thermal comfort: A review of concepts, roles, and system integration

Zeng, X., Lyu, J., & Lian, Z. (2026) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2026.117429

Agentic building energy management systems: adoption and design insights from surveys and interviews with building operators

Choi, H., Han, S., Han, G., Seo, B., Jung, D. E., & Kim, J. (2026) · Energy and Buildings · doi:10.1016/j.enbuild.2026.118165

G-Agent: A Large Language Model-Driven Multi-Agent Framework for Automated Configuration Optimization of Scramjet Cavity Combustors

Fang, Y., Xiong, D., Zhao, G., Yang, Y., Wang, H., & Sun, M. (2026) · Aerospace · doi:10.3390/aerospace13090790

Tool-augmented vs. autonomous LLM agents for building energy model modifications: A comparative study using NECB compliance models

Shirzadi, N., Lopez, P., & Stylianou, M. (2026) · Energy and AI · doi:10.1016/j.egyai.2026.100896

LLM-BEM-engineer: a large language model-based multi-agent AI engineer for automated building energy modeling and analysis

Jiang, G. Y., & Chen, J. (2026) · Advanced Engineering Informatics · doi:10.1016/j.aei.2026.105209

Generative AI-Driven Architectural and Structural Engineering Designs: A Review

Yujue, W., & Liao, W. (2026) · Journal of Intelligent Construction · doi:10.26599/jic.2026.9180126

OpenStudio-MCP: a model context protocol (MCP) server for AI agent-driven building energy modeling with the OpenStudio SDK

Ball, B. L., Long, N. L., Fleming-Dutra, K. E., & Goldwasser, D. (2026) · SoftwareX · doi:10.1016/j.softx.2026.103020

Automated BIM-to-BEM reconstruction for EnergyPlus thermal load simulation using BIM-derived room boundary data

Zhu, Y., Gu, J., Gong, C., Li, D., & Xu, P. (2026) · Energy and Buildings · doi:10.1016/j.enbuild.2026.118264

Generative AI for Building Industry: Applications, Challenges, and Future Directions

Le, A. T., Ahn, Y., & Nguyen, A. T. (2026) · Lecture notes in civil engineering · doi:10.1007/978-981-95-6770-6_90

An AI-assisted workflow for generative building design and energy modeling: Converting natural language design intent and images into 3D building energy model inputs

Wen, S., & Chen, Q. (2026) · Advanced Engineering Informatics · doi:10.1016/j.aei.2026.105313

Semantic-aware robust PV forecasting based on a localized enhanced LLM framework

Xiao, W., Zhang, W., Li, X., Wei, W., Chen, Y., & Xu, Z. (2026) · Applied Energy · doi:10.1016/j.apenergy.2026.128863

Explainability and Reliability in Large Language Model Systems: A Survey of SHAP, LIME and Attention Frameworks

Mary Shakkina, C., & Travis, M. L. (2026) · International Journal of Modern Science and Research Technology · doi:10.67317/ijmsrt26sep093

Towards Trustworthy Agentic Building Energy Modelling: The Role of Model Context Protocol

Zhou, J., Wang, M., Ren, C., & Tang, R. (2026) · Energy Use · doi:10.59717/ipj.energy-use.2026.100064

Large Language Models for Next-Generation Building Energy Management: From Task-Specific AI to General-Purpose AI

Zhang, C., Geng, Y., Zhang, J., Wang, F., & Chen, Q. (2026) · Energy Use · doi:10.59717/ipj.energy-use.2026.100063

Large language model agents capture heterogeneous occupant behaviors in building energy simulation of shared office spaces

Xu, X., Huang, G., Li, Z., & Mitoulis, S.-A. (2026) · Building and Environment · doi:10.1016/j.buildenv.2026.115329

SCAle-BEM: self- and cross-consistent agentic LLM/VLM framework for geometry identification and automated building energy modeling

Jiang, G., & Chen, J. (2026) · Energy and Buildings · doi:10.1016/j.enbuild.2026.118314

MAESTRO: Fine-Tuned Multiagent Large Action Model-Based GenAI Assistant Integrating IoT Sensing, Smart Control, and Extended Reality for Human-Building Interaction

Poudel, O., Assaad, R. H., Park, A. B., & Awada, M. (2026) · Journal of Computing in Civil Engineering · doi:10.1061/jccee5.cpeng-7915

DELFT-UBEM: An automated database-driven pipeline for district-scale building energy modeling, demonstrated on Dutch district retrofit

Jalilzadeh, A., Rafiee, A., Konstantinou, T., de Graaf, S., Scholten, H. J., & van Oosterom, P. J. M. (2026) · Energy and Buildings · doi:10.1016/j.enbuild.2026.118345

Automated Modelica Model Repair Using Generative AI

Sadrnezhaad, M. (2026) · ACM/IEEE International Conference on Model Driven Engineering Languages and Systems (MODELS) · doi:10.1145/3837062.3839546

Epeditor: A scalable platform for large-ensemble building energy simulation enabling agent-assisted automation

Xiao, J., Wang, Q., Zhou, H., Zhao, Y., & Lin, B. (2026) · Applied Energy · doi:10.1016/j.apenergy.2026.129029

Trustworthy LLM-Assisted Static Modeling of Hybrid AC/DC Distribution SystemsPreprint

Luo, S., Zhao, T., Han, R., & Li, G. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7610488

End-to-end climate-aware agentic workflow for urban building energy modelling enabled by large language models

Zhou, J., Dou, J., Wen, Y., Dong, B., Wang, K., Jing, R., Zhou, X., & Wang, M. (2026) · Advanced Engineering Informatics · doi:10.1016/j.aei.2026.105384

Artificial intelligence applications in building computational transformation: A systematic review from cradle to use

Zhuang, D., Jia, Y., Leng, Y., Guo, C., Xu, F., Zhang, W., Dong, Q., & Sun, C. T. (2026) · EBES Sustainable Building · doi:10.1016/j.ebesb.2026.100012

Twin model: A continuous learning method for short-term online forecasting of office building energy consumption considering extreme scenarios

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Localized LLM selection for agentic building energy modeling worker routing

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Inverse building envelope design via a physics-constrained dual-head language model

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Supporting building thermal resilience in early design stages: A framework integrating machine learning and large language models

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

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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

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

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 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