Dr. Brandon Benton is a researcher in the Strategic Energy
Analysis Center at the National Renewable Energy Laboratory. His research falls
broadly into wind and solar modeling, with a specific focus on renewable
resource availability. He uses a combination of conventional physics-based
simulations and machine learning to generate wind and solar data at
spatiotemporal scales needed for energy systems planning. Examples of this data
include Sup3rWind/Sup3rCC, generated with the "Super-Resolution for
Renewable Energy Resource Data" or "Sup3r" framework, and the
National Solar Radiation Database. Before joining NREL, Brandon completed his
Ph.D. in physics at Cornell University, researching the impact of volcanic
eruptions on hurricane formation.
Dr. Brandon Benton is a researcher in the Strategic Energy
Analysis Center at the National Renewable Energy Laboratory. His research falls
broadly into wind and solar modeling, with a specific focus on renewable
resource availability. He uses a combination of conventional physics-based
simulations and machine learning to generate wind and solar data at
spatiotemporal scales needed for energy systems planning. Examples of this data
include Sup3rWind/Sup3rCC, generated with the "Super-Resolution for
Renewable Energy Resource Data" or "Sup3r" framework, and the
National Solar Radiation Database. Before joining NREL, Brandon completed his
Ph.D. in physics at Cornell University, researching the impact of volcanic
eruptions on hurricane formation.
Dr. Andrew Glaws is a researcher in applied mathematics at the Computational Science Center at the National Renewable Energy Laboratory. His research focuses on enhancing scientific research into renewable energy and energy-efficient problems using machine learning, artificial intelligence, and other data-driven methods. He has collaborated with domain scientists in a variety of energy-related fields, including wind and solar energy, climate science, buildings energy analysis, bioenergy, and battery technology. Before joining NREL, Andrew completed his Ph.D. in computer science at the University of Colorado Boulder, researching the use of parameter reduction methods for computational experiments.
Dr. Ryan King is a senior scientist in the Complex Systems Simulation & Optimization Group within the Computational Science Center at the National Renewable Energy. His research interests include scientific machine learning, uncertainty quantification, and turbulent flows. Dr. Ryan King received his Ph.D. in mechanical engineering from the University of Colorado, Boulder. During his Ph.D., Ryan developed adjoint optimization techniques to improve wind plant design and created a new data-driven machine learning closure for turbulence modeling in large eddy simulations. Before graduate school, Ryan worked as an engineer at RES Americas, where he was involved in the design and construction of over 750 MW of operational wind energy.
