PNNL’s pioneering CETC project with regional universities demonstrates transactive controls among multiple commercial buildings and devices for energy efficiency and grid reliability.

By improving the Weather Research and Forecasting (WRF)-Solar model, this project aims to reduce forecast errors, improve sub-grid scale variability estimates, and more accurately estimate forecast uncertainty.

PNNL delivers workshops, consultations, and trainings for foreign partners to strengthen their implementation of international and multilateral sanctions regimes.

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.

RadAnalysis is a software developed by the Department of Energy to assist in the packaging and transportation of radioactive materials. It facilitates compliance with regulations, providing consistency, accuracy, and relevant documentation.

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.