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.
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.
The RD2C laboratory-directed research initiative seeks to develop resilient, adaptive, and intelligent sensing and control algorithms through the observational understanding and characterization of CPSs under adverse conditions.
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.
