As the world increasingly shifts towards wind and solar energy generation and the electrification of energy consumption, the dependence of society on weather conditions is becoming more apparent. However, with the climate changing rapidly, extreme weather events are becoming a common occurrence.
Energy system planners and operators require detailed and high-precision data on future climatic trends to understand how climate change will impact energy production, electricity demand, and other parameters. While there are indications of an increase in energy consumption, there is a lack of highly detailed resources to assess these impacts.
Grant Buster, a specialist at the National Renewable Energy Laboratory (NREL) in the United States, envisions a future where renewable sources will meet almost all electricity demand. He emphasizes the need to understand how renewable resources like wind and solar could be affected by climate change and how they can meet future energy needs.
To address this need, NREL employees Brandon Benton, Andrew Glose, Ryan King, and Grant developed the Super-ReSolution for Renewable Energy Resource Data Climate Change Impact (SUP3RCC) model, as described in an article published in Nature Energy.
SUP3RCC is an open model that utilizes generative machine learning methods to create advanced and highly accurate data sets, which are accessible to everyone for free. This model outperforms existing methods by providing detailed forecasts 40 times faster than traditional approaches.
Dan Billello, Director of NREL, views SUP3RCC as a significant advancement in studying and planning future energy systems. The tool generates essential climate data that can be integrated into energy system models, providing crucial information for ensuring energy supply reliability.
NREL’s community of modelers and analysts is working to bridge the gap between energy and climatology. By combining expertise in energy modeling and climate science, they aim to effectively utilize climatic information for designing and operating energy systems.