Super Resolution for Renewable Energy Resource Data With Wind From Reanalysis Data and Application to Ukraine

TL;DR

This paper introduces a deep learning GAN-based method for high-resolution wind data downscaling from reanalysis data, achieving comparable accuracy to traditional methods but with significantly reduced computational costs, demonstrated over Eastern Europe.

Contribution

The paper presents a novel GAN-based downscaling approach using true low-resolution simulation outputs, reducing computational costs by two orders of magnitude compared to dynamical methods.

Findings

Achieved comparable accuracy to dynamical downscaling in historical wind data

Reduced computational costs by two orders of magnitude

Generated a 24-year high-resolution wind dataset for Eastern Europe

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. 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).