Colloquium (as part of NREL day)

Accurate and high-resolution data reflecting different climate scenarios are vital for policy makers when deciding on the development of future energy resources, electrical infrastructure, transportation networks, agriculture, and many other societally important systems. However, state-of-the-art long-term global climate simulations are unable to resolve the spatiotemporal characteristics necessary for resource assessment or operational planning. We introduce an adversarial deep learning approach to super resolve wind velocity and solar irradiance outputs from global climate models to scales sufficient for renewable energy resource assessment. We demonstrate up to a 50x spatial or 24x temporal resolution enhancement of wind and solar data. Additionally, we demonstrate a conditional sampling formulation and new diversity loss that allows for generation of multiple super resolved fields conditioned on the same low resolution input. We conclude with a super-resolution study of renewable energy resources based on climate scenario data from the Intergovernmental Panel on Climate Change’s Fifth Assessment Report.

Hybrid:  Math, 501 and Zoom https://arizona.zoom.us/j/86997964863     Password:  Locute