Liang Zhang joined NREL in 2019. As a member of the Commercial Buildings Research Group, he works on the ComStock project, which uses multiple data sources, statistical sampling methods, and advanced building energy simulations to estimate energy use of the U.S. commercial building stock. He also explores the use of artificial intelligence and machine learning techniques in automated fault detection/diagnostics and building energy modeling.
Prior to joining NREL, Liang was a graduate researcher at Drexel University, working on DOE and NSF funded projects in the field of transactive load control, building-to-grid integration, and data-driven building energy modeling for model predictive control.
Transactive load control and building-to-grid integration
Automated fault detection and diagnostics of building energy systems
Data-driven building energy modeling
Machine learning in buildings
Building energy efficiency and sustainability
Ph.D. Architectural Engineering, Drexel University
M.S. Power Engineering, Tongji University
B.S. Electronic Science and Technology, East China Normal University
A systematic feature selection procedure for short-term data-driven building energy forecasting model development, Energy and Buildings (2019)
Apply active learning in short-term data-driven building energy modeling, International High Performance Buildings Conference (2018)