Chao Ding is a project scientist in the International Energy Analysis Department at Lawrence Berkeley National Lab. His research interests are energy efficiency for Heating, Ventilation, Air conditioning and Refrigeration (HVAC&R) system, natural ventilation, building performance modeling, and machine learning. His current research projects include high-efficiency low-GWP air conditioner development; Statistics development and validation for the Building Efficiency Targeting Tool for Energy Retrofits (BETTER); Computational fluid dynamics (CFD) modeling; Machine learning for urban geometry generation etc.
He was awarded a 2020 R&D 100 Award and an LBNL Director’s Award for Exemplary Achievement in Technology Transfer for development of the BETTER tool.
He holds a Ph.D. in Building Performance and Diagnostics from Carnegie Mellon University, an M.S. in Mechanical Engineering from Carnegie Mellon University and an M.S. in HVAC from Tongji University, China.