Chao Ding

Chao Ding

Materials Project Scientist/Engineer
(510) 486-6835

Bio

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. 

Awards

Spot: Han Li, Chao Ding -  May 25th 2021

Excellent contributions to the 2021 EarthX Climate-Tech Prize pitch deck and in E-Capital Summit investor meetings.

Han Li profile page

Chao Ding profile page

2020 R&D 100 Award: BETTER Tool -  October 05th 2020

 

Building Efficiency Targeting Tool for Energy Retrofits (BETTER)

The buildings sector is the largest source of primary energy consumption (40%) and ranks second after the industrial sector as a global source of direct and indirect carbon dioxide emissions from fuel combustion. According to the World Economic Forum, nearly one-half of all energy consumed by buildings could be avoided with new energy-efficient systems and equipment.

The Building Efficiency Targeting Tool for Energy Retrofits (BETTER) allows municipalities, building and portfolio owners and managers, and energy service providers to quickly and easily identify the most effective cost-saving and energy-efficiency measures in their buildings. With an open-source, data-driven analytical engine, BETTER uses readily available building and monthly energy data to quantify energy, cost, and greenhouse gas reduction potential, and to recommend efficiency interventions at the building and portfolio levels to capture that potential.

It is estimated that BETTER will help reduce about 165.8 megatons of carbon dioxide equivalent (MtCO2e) globally by 2030. This is equivalent to the CO2 sequestered by growing 2.7 billion tree seedlings for 10 years.

The development team includes Berkeley Lab scientists Nan Zhou, Carolyn Szum, Han Li, Chao Ding, Xu Liu, and William Huang, along with collaborators from Johnson Controls and ICF.

 

2020 Tech Transfer Award: BETTER Team -  September 29th 2020

2020 Director’s Awards for Exceptional Achievement, Tech Transfer

In recognition of the exemplary efforts of Carolyn Szum, Chao Ding, Nan Zhou, Xu Liu, Han Li to build important relationships with industry to advance the science of data-driven, remote building energy analysis to improve building energy efficiency at speed and scale worldwide.

Publications

2021

2020

2019

2017