About Us +
Researchers in the Building Technology & Urban Systems Division (BTUS) at Lawrence Berkeley National Laboratory develop data and technologies that increase energy efficiency and improve the health, safety and comfort of building occupants, in the United States and worldwide.

We work closely with industry partners, academics and government officials to achieve these goals, and share our research widely
Research +
We are at the forefront of cutting-edge research that redefines building technology and explores all areas of urban systems.

We have been leaders for decades in developing energy-efficient windows, improving indoor air quality, coming up with new ideas to fix the nation's electricity grid, and so much more.

Visit our research areas at the right to find out more.
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Publications +
Enjoy presentations from Building Technology & Urban systems research experts on a wide variety of topics in the areas of building energy efficiency, the electricity grid and how it relates to buildings and much more.
- Presentations
News
Tools & Guides +
Explore our tools, guidebooks and software and download for free.

We offer a variety of technologies designed to simulate and model real-world circumstances to assist in energy-saving programs and help building owners build better buildings. These tools can help calculate performance of building systems like windows and shades, help consumers and builders pick the best windows for a variety of applications and much more.

Publications

Publications By Research Area

2024

Casillas, Armando, Yimin Chen, Jessica Granderson, Guanjing Lin, Zhelun Chen, Jin Wen, and Sen Huang."Development of high-fidelity air handling unit fault models for FDD innovation: lessons learned and recommendations."Journal of Building Performance Simulation 17.5 (2024) 615-630. DOI

2023

Granderson, Jessica, Guanjing Lin, Yimin Chen, Armando Casillas, Jin Wen, Zhelun Chen, Piljae Im, Sen Huang, and Jiazhen Ling."A labeled dataset for building HVAC systems operating in faulted and fault-free states."Scientific Data 10 (2023). DOI

Chen, Zhelun, Zheng O'Neill, Jin Wen, Ojas Pradhan, Tao Yang, Xing Lu, Guanjing Lin, Shohei Miyata, Seungjae Lee, Chou Shen, Roberto Chiosa, Marco Savino Piscitelli, Alfonso Capozzoli, Franz Hengel, Alexander Kuhrer, Marco Pritoni, Wei Liu, John Claub, Yimin Chen, and Terry Herr."A review of data-driven fault detection and diagnostics for building HVAC systems."Applied Energy Vol.339 No..121030 (2023). DOI

2022

Chen, Yimin, Jin Wen, Ojas Pradhan, James Lo, and Teresa Wu."Using discrete Bayesian networks for diagnosing and isolating cross-level faults in HVAC systems."Applied Energy 327 (2022). DOI

Chen, Yimin, Guanjing Lin, Zhelun Chen, Jin Wen, and Jessica Granderson."A simulation-based evaluation of fan coil unit fault effects."Energy and Buildings 263 (2022). DOI

Chen, Chien-fei, Thomas Dietz, Nina H Fefferman, Jamie Greig, Kristen Cetin, Caitlin Robinson, Laura Arpan, Marcel Schweiker, Bing Dong, Wenbo Wu, Yue Li, Hongyu Zhou, Jianzhong Wu, Jin Wen, Joshua S Fu, Tianzhen Hong, Da Yan, Hannah Nelson, Yimin Zhu, Xueping Li, Le Xie, and Rachel Fu."Extreme events, energy security and equality through micro- and macro-levels: Concepts, challenges and methods."Energy Research & Social Science 85 (2022) 102401. DOI

Chen, Yimin, Jin Wen, and James Lo."Using Weather and Schedule based Pattern Matching and Feature based PCA for Whole Building Fault Detection — Part II Field Evaluation."Journal of Engineering for Sustainable Buildings and Cities (2022). DOI

Chen, Yimin, Zhelun Chen, Guanjing Lin, Jin Wen, and Jessica Granderson."A simulation-based method to analyze fan coil unit fault impacts."2022 ASHRAE Summer Conference (2022).