PNNL

GLIMPSE of Future Power Grid Models

GMI-DRL: Empowering Multi-GPU DRL with Adaptive-Grained Parallelism

DS-GL: Advancing Graph Learning via Harnessing the Power of Nature within Dynamic Systems

Extending Power of Nature from Binary Problems to Real-Valued Graph Learning in Real World

Nature-GL: A Revolutionary Learning Paradigm Unleashing Nature’s Power in Real-World Spatial-Temporal Graph Learning

Deep Learning Trains Buildings to Optimize Efficiency

New approach allows nonexperts to optimize control of a building's energy systems without adding computing power or proprietary software.

Expanding Data Tools for a Resilient Grid

Expanding the nation’s largest wind power plant time series database; helping grid planners navigate a resilient energy future with better data.

An Ally for Alloys

Machine learning techniques are accelerating the development of stronger alloys for power plants, which will yield efficiency, cost, and decarbonization benefits.

AI Ups Response Time when the Grid Goes Down

Trouble on the electric grid might start with something relatively small: a downed power line, or a lightning strike at a substation. What happens next?

PNNL, Sandia, and Georgia Tech Join Forces in AI Effort

Three powerhouses in the realm of artificial intelligence have become partners in a new research center created by the U.S. Department of Energy.