Han Li
Han Li
Bio
Mr. Han Li is a Senior Scientific Engineering Associate at Lawrence Berkeley National Laboratory. His work focuses on building energy modeling and performance evaluations, machine learning for building dynamics modeling and controls, and software development, which are primarily funded by DOE. Aside from his work at LBNL, Han is currently a PhD candidate in Building Performance and Diagnostics at Carnegie Mellon University. He has a Bachelor's degree in Mechanical Engineering from Tongji University in China.
Education
Awards
2022 R&D 100 Award: City Buildings, Energy, and Sustainability (CityBES) Web Tool for Climate Change Strategies - August 22nd 2022
Buildings generate 39% of global CO2 emissions, but evaluating and prioritizing cost-effective technical solutions for individual buildings at city scale poses a significant challenge for city stakeholders. CityBES is a free, powerful modeling and analysis tool that enables quick and quantitative assessments of actionable recommendations for decarbonizing buildings and improving their thermal resilience against extreme weather at the urban scale. CityBES builds upon open city datasets, international data standards, the powerful EnergyPlus simulation engine, a library of mitigation and adaptation measures, and 3D-GIS visualization to inform decision making on city buildings, energy, and sustainability.
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.
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.