The U.S. Department of Energy-sponsored Internet of Things Upgradeable Lighting Challenge is designed to encourage the widespread adoption of IoT-Upgraded Lighting.
PNNL administers two research buoys for the U.S. Department of Energy that allows collection of wind meteorological and oceanographic data off the nation's coasts.
Physics Informed Machine Learning (PIML) is a modeling approach that harnesses the power of machine learning and big data to improve the understanding of coupled, dynamic systems.
PNNL is working on behalf of the U.S. Department of Energy to create a prototype system that enables homes to help provide services to the power grid while delivering economic benefits to residents.
A software suite for working with neutron activation rates measured in a nuclear fission reactor, an accelerator-based neutron source, or any neutron field to determine the neutron flux spectrum using a generalized least-squares approach.
PNNL researchers developed and manage the online database Tethys to actively collects and curates information on the environmental effects of wind and marine energy.
