PNNL researchers develop software that uses geographical data to build a free, open-source grid reference system to provide a precise system to locate structures.
New building energy codes could reduce utility bills by $138 billion and prevent 900 million metric tons of CO2 emissions coming from buildings. Now, they will be easier to adopt.
PNNL will play a key role in advancing Connected Communities made up of efficient homes and buildings that communicate with the grid to produce energy and environmental benefits.
Researchers at PNNL examined heat pump water heater (HPWH) operation in Pacific Northwest residences, gaining insights into HPWH electricity use patterns. Part of the study captured trends during a COVID-19 stay-at-home order.
A Q&A with Lauren Charles, veterinarian and PNNL data scientist, on zoonotic diseases and the role biosurveillance plays in mitigating the growing threat to global health.
PNNL’s data-infused approach to electron microscopes’ use in scientific experimentation will help researchers and industry interpret large data streams and drive down costs.
PNNL combines AI and cloud computing with damage assessment tool to predict path of wildfires and quickly evaluate the impact of natural disasters, giving first responders an upper hand.
Machine learning techniques are accelerating the development of stronger alloys for power plants, which will yield efficiency, cost, and decarbonization benefits.
PNNL’s energy-efficient dehumidifier may reduce energy consumption by up to 50% in residential A/C systems and increase the range of electric vehicles by up to 75%. The system has been licensed to Montana Technologies.
The first customized resource of its kind, H-BEST analyzes the indoor environmental quality profile for buildings and helps its users identify the costs and benefits of improvements.
National Nuclear Security Administration Graduate Fellow Marc Wonders has spent the past year working with researchers exploring artificial intelligence in the national security mission space.
A webapp developed by PNNL in collaboration with the University of Washington to help drive efficiencies for urban delivery drivers is now in the prototype stage and ready for testing.