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

2022

Liu, Jingjing, Rongxin Yin, Lili Yu, Mary Ann Piette, Marco Pritoni, Armando Casillas, Jiarong Xie, Tianzhen Hong, Monica Neukomm, and Peter Schwartz."Defining and applying an electricity demand flexibility benchmarking metrics framework for grid-interactive efficient commercial buildings."Advances in Applied Energy 8 (2022) 100107. DOI

2021

Li, , Han, Zhe Wang, Tianzhen Hong, and Mary Ann Piette."Energy flexibility of residential buildings: A systematic review of characterization and quantification methods and applications."Advances in Applied Energy 3 (2021) 100054. DOI

Wang, Zhe, Tianzhen Hong, and Han Li."Informing the planning of rotating power outages in heat waves through data analytics of connected smart thermostats for residential buildings."Environmental Research Letters 16.7 (2021) 074003. DOI

2020

Cho, Brian, Teresa Dayrit, Yuan Gao, Zhe Wang, Tianzhen Hong, Alex Sim, and Kesheng."Effective Missing Value Imputation Methods for Building Monitoring Data."IEEE BigData 2020 (2020). DOI

2019

Wang, Zhe, Tianzhen Hong, and Mary Ann Piette."Predicting plug loads with occupant count data through a deep learning approach."Energy 181 (2019) 29 - 42. DOI

Wang, Wei, Tianzhen Hong, Xiaodong Xu, Jiayu Chen, Ziang Liu, and Ning Xu."Forecasting district-scale energy dynamics through integrating building network and long short-term memory learning algorithm."Applied Energy 248 (2019) 217 - 230. DOI

Wang, Zhe, Tianzhen Hong, and Mary Ann Piette."Data fusion in predicting internal heat gains for office buildings through a deep learning approach."Applied Energy 240 (2019) 386 - 398. DOI

Wang, Wei, Tianzhen Hong, Nan Li, Ryan Qi Wang, and Jiayu Chen."Linking energy-cyber-physical systems with occupancy prediction and interpretation through WiFi probe-based ensemble classification."Applied Energy 236 (2019) 55 - 69. DOI

2018

Piette, Mary Ann, Rebecca Zarin Pass, Reshma Singh, and Tianzhen Hong."Review of City Energy and Emissions Analysis Needs, Methods & Tools."2018 Summer Study on Energy Efficiency in Buildings (2018). DOI

2008

Hong, Tianzhen, Dale A. Sartor, Paul A. Mathew, and Mehry Yazdanian."Comparisons of HVAC Simulations between EnergyPlus and DOE-2.2 for data centers."ASHRAE Conference 115 Part 1 (2008).