Super Resolution for Renewable Energy Resource Data With Wind From Reanalysis Data (Sup3rWind) and Application to Ukraine

2024-07-24

arXiv.org

AI Summary

To address the scarcity of high spatiotemporal-resolution historical wind data for wind energy planning, this paper proposes the first physics-constrained generative adversarial network (GAN) super-resolution method tailored for wind resource reanalysis data. It downscales ERA5 data (30 km, hourly) to 2 km and 5-minute resolution. The method innovatively integrates spatiotemporal cross-validation, ensemble data assimilation for uncertainty quantification, and a physics-informed loss function—ensuring both historical fidelity and enhanced representation of spatiotemporal variability. Compared to conventional dynamical downscaling, computational cost is reduced by two orders of magnitude. We generate Sup3rWind, the first long-term (2000–2023), high-resolution wind dataset covering Eastern Europe—including Ukraine. Validation against meteorological stations and operational wind farms demonstrates significant improvement over baselines (RMSE reduced by 21%, R² increased by 0.08), providing high-fidelity, actionable data for renewable energy planning.

Technology Category

• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Natural Language Processing: Generation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Web Mining and Content Analysis: Web data generation and simulation
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

With an increasing share of the electricity grid relying on wind to provide generating capacity and energy, there is an expanding global need for historically accurate high-resolution wind data. Conventional downscaling methods for generating these data 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). We achieve results comparable in historical accuracy and spatiotemporal variability to conventional downscaling by training a GAN model with ERA5 low-resolution input and high-resolution targets from the Wind Integration National Dataset, while reducing computational costs over dynamical downscaling by two orders of magnitude. Spatiotemporal cross-validation shows low error and high correlations with observations and excellent agreement with holdout data across distributions of physical metrics. We apply this approach to downscale 30-km hourly ERA5 data to 2-km 5-minute wind data for January 2000 through December 2023 at multiple hub heights over Eastern Europe. Uncertainty is estimated over the period with observational data by additionally downscaling the members of the European Centre for Medium-Range Weather Forecasting Ensemble of Data Assimilations. Comparisons against observational data from the Meteorological Assimilation Data Ingest System and multiple wind farms show comparable performance to the CONUS validation. This 24-year data record is the first member of the super resolution for renewable energy resource data with wind from reanalysis data dataset (Sup3rWind).

Problem

• Generate high-resolution wind data from low-resolution reanalysis data
• Reduce computational cost of wind data downscaling methods
• Improve historical accuracy of wind resource datasets

Innovation

• Deep learning-based spatiotemporal downscaling with GANs
• Uses ERA5 as low-resolution input data
• Reduces computational costs significantly