TensorBuild Lab, led by Prof. Liang Zhang, is an interdisciplinary research group in the Department of Civil and Architectural Engineering and Mechanics (CAEM) at the University of Arizona (U of A).
TensorBuild Lab advances science, engineering, and education toward an energy-efficient and sustainable evolution of smart buildings and cities in the global challenges of climate change and grid transformation with a particular focus on AI innovations.
Research Area
Featured News
May 5, 2025, Paper Published
Zhang, L., Fu, X., Li, Y., & Chen, J. (2025). Large language model-based agent Schema and library for automated building energy analysis and modeling. Automation in Construction, 176, 106244. (link)
February 27, 2025, Paper Published
Zhang, L., & Chen, Z. (2025). Opportunities of applying Large Language Models in building energy sector. Renewable and Sustainable Energy Reviews, 214, 115558. (link)
January 16, 2025, Paper Published
Jiang, G., Ma, Z., Zhang, L., & Chen, J. (2025). Prompt engineering to inform large language model in automated building energy modeling. Energy, 316, 134548. (link)
January 17, 2025, Paper Published
Liu, M., Zhang, L., Chen, J., Chen, W. A., Yang, Z., Lo, L. J., … & O’Neill, Z. (2025, January). Large language models for building energy applications: Opportunities and challenges. In Building Simulation (pp. 1-10). Beijing: Tsinghua University Press. (link)
November 26, 2024, Paper Published
Zhang, L., Ford, V., Chen, Z., & Chen, J. (2025). Automatic building energy model development and debugging using large language models agentic workflow. Energy and Buildings, 327, 115116.v (link)
September 13, 2024, Paper Published
Zhang, L., Chen, Z., & Ford, V. (2024). Advancing Building Energy Modeling with Large Language Models: Exploration and Case Studies. (link)
August 1, 2024, Funding & Project
TensorBuild Lab is delighted to announce the participation in the Bridging Gaps Towards a Sustainable Energy Future by Applying Human Factors in Transportation-Building Integration project, funded by RII 2024 UArizona National Labs Partnerships Grants. Spearheaded/lead by Prof. Alyssa Ryan and co-lead by Prof. Liang Zhang, this award-winning project is set to deliver human behavior models, particularly in the integrated domains of building and transportation energy use modeling, as part of the broader endeavor toward decarbonization.
July 29, 2024, New Paper Published
Zhang, L., Kaufman, Z., & Leach, M. (2024). Physics-informed hybrid modeling methodology for building infiltration. Energy and Buildings, 114580. (link)
May 10, 2024, Paper Published
Zhang, L., & Chen, Z. (2024). Large language model-based interpretable machine learning control in building energy systems. Energy and Buildings, 313, 114278. (link)
February 5, 2024, Funding & Project
TensorBuild Lab is delighted to announce the subcontract Technical Support for Empirical Validation Data Analysis and Specification Development funded by the U.S. Department of Energy and National Renewable Energy Laboratory. The Subcontractor will foster a collaborative partnership with the academic community and NREL staff to advance energy-related research and engage faculty and students. Key responsibilities include utilizing machine learning for building energy modeling and control, providing expertise in fault detection and diagnostics to reduce energy wastage, contributing to the development of smart and connected communities through advanced controls and modeling to identify energy and carbon reduction strategies, and offering analytical support in foundational building energy modeling to underpin various projects within the building sector.
August 23, 2024, Lab Member Update
We warmly welcome Xiaoqin Fu, who has recently joined the TensorBuild Lab as a graduate research assistant!
May 27, 2024, Paper Published
Jiang, G., Ma, Z., Zhang, L., & Chen, J. (2024). EPlus-LLM: A large language model-based computing platform for automated building energy modeling. Applied Energy, 367, 123431.
January 16, 2024, Funding & Project
TensorBuild Lab is delighted to announce the AI-Enhanced Interactive Data Infrastructure project, a transformative initiative to decarbonize building and transportation sectors using Large Language Models like GPT-4 for data analysis and interaction. Spearheaded by Prof. Liang Zhang, this award-winning project funded by FY24 Innovative Projects in Energy Grants, is set to deliver an interactive data framework and a comprehensive case study for the City of Tucson, advancing sustainability efforts and research capabilities at University of Arizona.
January 15, 2023, Paper Published
Zhang, L., Leach, M., Chen, J., & Hu, Y. (2023). Sensor cost-effectiveness analysis for data-driven fault detection and diagnostics in commercial buildings. Energy, 263, 125577. (link)
Feb 5, 2024, Lab Member Update
We warmly welcome Amir Hosain Sharif, who has recently joined the BESLab team as a Postdoctoral Researcher!
September 30, Lab Member Update
Prof. Liang Zhang starts his joint appointment with National Renewable Energy Laboratory to promote research and education in building decolonization with advanced modeling and AI technologies.
March 21, 2023, Paper Published
Zhang, L., Chen, Z., Zhang, X., Pertzborn, A., & Jin, X. (2023, March). Challenges and opportunities of machine learning control in building operations. In Building Simulation (pp. 1-22). Beijing: Tsinghua University Press. (link)
October 15, 2022, Paper Published
Zhang, L., Leach, M., Chen, J., & Hu, Y. (2023). Sensor cost-effectiveness analysis for data-driven fault detection and diagnostics in commercial buildings. Energy, 263, 125577. (link)