Super-Resolution for Renewable Energy Resource Data with Wind from Reanalysis Data and Application to Ukraine

Brandon N. Benton (), Grant Buster, Pavlo Pinchuk, Andrew Glaws, Ryan N. King, Galen Maclaurin and Ilya Chernyakhovskiy
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Brandon N. Benton: National Renewable Energy Laboratory, Golden, CO 80401, USA
Grant Buster: National Renewable Energy Laboratory, Golden, CO 80401, USA
Pavlo Pinchuk: National Renewable Energy Laboratory, Golden, CO 80401, USA
Andrew Glaws: National Renewable Energy Laboratory, Golden, CO 80401, USA
Ryan N. King: National Renewable Energy Laboratory, Golden, CO 80401, USA
Galen Maclaurin: National Renewable Energy Laboratory, Golden, CO 80401, USA
Ilya Chernyakhovskiy: National Renewable Energy Laboratory, Golden, CO 80401, USA

Energies, 2025, vol. 18, issue 14, 1-21

Abstract: With a potentially increasing share of the electricity grid relying on wind to provide generating capacity and energy, there is an expanding global need for historically accurate, spatiotemporally continuous, high-resolution wind data. Conventional downscaling methods for generating these data based on numerical weather prediction have a high computational burden and require extensive tuning for historical accuracy. In this work, we present a novel deep learning-based spatiotemporal downscaling method using generative adversarial networks (GANs) for generating historically accurate high-resolution wind resource data from the European Centre for Medium-Range Weather Forecasting Reanalysis version 5 data (ERA5). In contrast to previous approaches, which used coarsened high-resolution data as low-resolution training data, we use true low-resolution simulation outputs. We show that by training a GAN model with ERA5 as the low-resolution input and Wind Integration National Dataset Toolkit (WTK) data as the high-resolution target, we achieved results comparable in historical accuracy and spatiotemporal variability to conventional dynamical downscaling. This GAN-based downscaling method additionally reduces computational costs over dynamical downscaling by two orders of magnitude. We applied this approach to downscale 30 km, hourly ERA5 data to 2 km, 5 min wind data for January 2000 through December 2023 at multiple hub heights over Ukraine, Moldova, and part of Romania. With WTK coverage limited to North America from 2007–2013, this is a significant spatiotemporal generalization. The geographic extent centered on Ukraine was motivated by stakeholders and energy-planning needs to rebuild the Ukrainian power grid in a decentralized manner. This 24-year data record is the first member of the super-resolution for renewable energy resource data with wind from the reanalysis data dataset (Sup3rWind).

Keywords: machine learning; downscaling; wind energy; ERA5; wind toolkit (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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