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SEPTEMBER 14, 2020
Web Feature

VOLTTRON™ Goes to School

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.

Secretary of Energy Advisory Board (SEAB) Report Recognizes PNNL Contributions

ML and AI

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.

View full preliminary findings of the Secretary of Energy Advisory Board (SEAB) report.

For more information about PNNL’s research contributions, contact Aaron Luttman

JULY 14, 2020
Web Feature

Turning the Tides

Their consistency and predictability makes tidal energy attractive, not only as a source of electricity but, potentially, as a mechanism to provide reliability and resilience to regional or local power grids.
NOVEMBER 26, 2019
Web Feature

Conquering Peak Power

PNNL’s Intelligent Load Control technology manages and adjusts electricity use in buildings when there’s peak demand on the power grid.

PNNL Launches Marine Renewable Energy Database

Logo of Tethys Engineering

PNNL created an online database to share information related to the marine renewable energy industry.

Tethys Engineering addresses industry’s technical and engineering challenges

November 18, 2019
November 18, 2019

Marine renewable energy (MRE) has the potential to provide 90 gigawatts of power in the United States through waves and tidal and ocean currents.

To harness the ocean’s energy, the MRE industry needs to understand how to address technical and engineering challenges such as efficient power takeoff, device survivability, and grid integration.

PNNL developed Tethys Engineering in September 2019 to allow sharing resources around the deployment of devices in corrosive, high-energy marine environments. The recently launched Tethys Engineering online database includes collected and curated documents surrounding the technical and engineering development of MRE devices. Users can search and filter results to intuitively identify information relevant to developers, researchers, and regulators.

Tethys Engineering includes more than 3,000 journal articles, conference papers, reports, and presentations related to wave, current, salinity gradient, and ocean thermal energy conversion technologies. The database contains information from around the world.

The Tethys Engineering database was created as a companion to the already established Tethys website, which focuses on the environmental effects of the MRE industry.

November 18, 2019
OCTOBER 31, 2019
Web Feature

The World’s Energy Storage Powerhouse

Pumped-storage hydropower offers the most cost-effective storage option for shifting large volumes of energy. A PNNL-led team wrote a report comparing cost and performance factors for 10 storage technologies.

Data Assimilation Impact of In Situ and Remote Sensing Meteorological Observations on Wind Power Forecasts during the First Wind Forecast Improvement Project (WFIP)

July 1, 2019
September 26, 2019
Journal Article


During the first Wind Forecast Improvement Project (WFIP) new meteorological observations were collected from a large suite of instruments, including wind velocities measured on networks of tall towers provided by wind industry partners, wind speeds measured by cup anemometers mounted on the nacelles of wind turbines, and by networks of Doppler sodars and radar wind profilers. Previous data denial studies found a significant improvement of up to 6% RMSE reduction for short-term wind power forecasts due to the assimilation of all of these observations into the NOAA Rapid Refresh (RAP) forecast model using a 3dvar GSI data assimilation scheme. As a follow-on study, we now investigate the impacts of assimilating into the RAP model either the additional remote sensing observations (sodars and wind profiling radars) alone, or assimilating the industry provided in situ observations (tall towers and nacelle anemometers) alone, in addition to the standard meteorological data sets that are routinely available. The more numerous tall tower/nacelle observations provide a relatively large improvement through the first 3-4 hours of the forecasts, which however decays to a negligible impact by forecast hour 6. In comparison the less numerous vertical profiling sodars/radars provide an initially smaller impact that decays at a much slower rate, with a positive impact present through the first 12 hours of the forecast. Large positive assimilation impacts for both sets of instruments are found during daytime hours, while small or even negative impacts are found during nighttime hours.

Research topics


Wilczak J.M., J. Olson, I. Djalaova, L. Bianco, L.K. Berg, W.J. Shaw, and R.L. Coulter, et al. 2019. "Data Assimilation Impact of In Situ and Remote Sensing Meteorological Observations on Wind Power Forecasts during the First Wind Forecast Improvement Project (WFIP)." Wind Energy 22, no. 7:932-944. PNNL-SA-132499. doi:10.1002/we.2332