Filtered by Chemical Physics, Dark Matter, Data Analytics & Machine Learning, Distribution, Energy Efficiency, Geothermal Energy, Graph Analytics, Grid Architecture, High-Performance Computing, Integrative Omics, Solar Energy, and Stakeholder Engagement
The PNNL-developed VOLTTRON™ software platform’s advancement has benefited from a community-driven approach. The technology has been used in buildings nationwide, including most recently on a university campus.
Making sure there’s enough electricity at the lowest price is a critical endeavor undertaken daily by electricity market operators. Now, there’s an approach that provides more timely and accurate information to make day-ahead decisions.
Secretary of Energy Advisory Board (SEAB) Report Recognizes PNNL Contributions
Report features how PNNL’s computing capabilities are affecting the nation’s security, science, and energy missions
August 25, 2020
August 25, 2020
Contributions from researchers across Pacific Northwest National Laboratory (PNNL) were recognized in the preliminary findings of a Secretary of Energy Advisory Board (SEAB) report from a working group dedicated to the U.S. Department of Energy’s (DOE’s) capabilities and future in artificial intelligence (AI) and machine learning. PNNL researchers’ expertise is prominent throughout DOE’s AI efforts, particularly in the areas of data sciences and national security.
Based largely on input from DOE sponsors, the report features how PNNL’s computing capabilities are affecting the nation’s security, science, and energy missions. Key highlights include:
Studying how AI affects the global landscape for securing nuclear materials, potentially using deep learning to enhance physical and digital protections against material concealment, delivery, theft, and sabotage.
Describing how the United States and its partners might employ deep learning to combat attack efforts for enhanced nuclear security.
Designing advanced deep learning models to characterize operations with buildings, using electrical signatures on power lines, enabling new designs for energy-efficient buildings in addition to enhanced security features for nuclear facilities.
Leading the nuclear explosive monitoring project with data scientists working to significantly lower detection thresholds of low-yield, evasive underground nuclear explosions without increasing time-to-detection or the amount of human analysis.
Co-design of advanced accelerator, memory and data movement concepts to support convergence of AI and machine learning methods with other forms of data analytics and traditional scientific high performance computing (HPC).
The report highlights PNNL’s support to the National Nuclear Security Administration, featuring joint laboratory collaborations between PNNL and others, including the Y-12 National Security Complex, Sandia National Laboratories, Lawrence Livermore National Laboratory, Los Alamos National Laboratory, and Oak Ridge National Laboratory. Additionally, PNNL is working as part of DOE’s comparative advantages in AI, providing the Office of Energy Efficiency and Renewable Energy access to AI subject matter experts.
Researchers from PNNL have helped colleagues at OHSU identify lipid molecules required for Zika infection in human cells. The specific lipids involved could also be a clue to why the virus primarily infects brain tissue.
PNNL study evaluated "tunable" lighting and its effects on sleep at study in a California nursing home. Tunable refers to the ability to adjust LED light output and the warmth or coolness of the light color.