Recognizing how innovation and clean technologies at the very edge of the grid can work together to transition the electricity system, PNNL takes a multidisciplinary approach to advancing and integrating renewable energy solutions.
PNNL computer scientists joined international leaders in machine learning to present research to detect and address potential cybersecurity threats and devise epidemic interventions.
Study says planners need to account for climate impacts on renewable energy during capacity development planning to fully understand investment implications to the power sector.
Sriram Krishnamoorthy, a computer scientist at PNNL, collaborated with a University of Utah team on a student computing research project that won Best Student Paper at SC20.
PNNL has published a report that sets the foundation for modeling gaps and technical challenges in optimizing hydropower operations for both energy production and water management.
California and other areas of the U.S. Southwest may see less future winter precipitation than previously projected by climate models, according to new research that corrects for a long-standing model error: the double-ITCZ bias.
James A. Ang, a PNNL computing expert, was recently invited to moderate a panel in a virtual workshop focused on federally funded research and development on software for heterogeneous computing.
Culminating 10 years of study, researchers at PNNL’s Marine and Coastal Research Laboratory developed a new predictive framework for estuarine–tidal river research and management.
In today’s digital age, the rabbit hole of connected information can be not only a time sink, but downright overwhelming. Even for high-performance computers.
For decades, the Department of Energy's Pacific Northwest National Laboratory has played a role in establishing and maintaining sustainable hydropower for the region.
