High-Resolution Meteorology with Climate Change Impacts from Global Climate Model Data Using Generative Machine Learning
As renewable energy generation increases, the impacts of weather and climate on energy generation and demand become critical to the reliability of the energy system. However, these impacts are often overlooked. Global climate models (GCMs) can be used to understand possible changes to our climate, but their coarse resolution makes them difficult to use in energy system modelling. Here we present open-source generative machine learning methods that produce meteorological data at a nominal spatial resolution of 4 km at an hourly frequency based on inputs from 100 km daily-average GCM data. These methods run 40 times faster than traditional downscaling methods and produce data that have high-resolution spatial and temporal attributes similar to historical datasets. We demonstrate that these methods can be used to downscale projected changes in wind, solar and temperature variables across multiple GCMs including projections for more frequent low-wind and high-temperature events in the Eastern United States.
- Research Organization:
- National Renewable Energy Laboratory (NREL), Golden, CO (United States)
- Sponsoring Organization:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office; USDOE National Renewable Energy Laboratory (NREL), Laboratory Directed Research and Development (LDRD) Program; USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
- DOE Contract Number:
- AC36-08GO28308; AC36-08GO28308; AC36-08GO28308; AC36-08GO28308
- OSTI ID:
- 2345176
- Report Number(s):
- NREL/JA-6A20-85462; MainId:86235; UUID:99b1d1b1-e348-48ca-8625-a111cd7143bb; MainAdminId:72515
- Journal Information:
- Nature Energy, Journal Name: Nature Energy Journal Issue: 7 Vol. 9
- Country of Publication:
- United States
- Language:
- English
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