基于再分析数据风场的可再生能源资源数据超分辨率方法及其在乌克兰的应用
Super Resolution for Renewable Energy Resource Data With Wind From Reanalysis Data and Application to Ukraine
AI总结：
本文提出基于GAN的时空降尺度方法，利用ERA5再分析数据生成高分辨率风资源数据，在保持历史准确性的同时将计算成本降低两个数量级，并应用于乌克兰等地区构建24年Sup3rWind数据集。
摘要：
随着电网中依赖风能提供发电容量和电力的比例可能不断增加，全球对历史上准确、时空连续、高分辨率风数据的需求日益增长。基于数值天气预报生成这些数据的传统降尺度方法计算负担高，且需要大量调参才能达到历史准确性。在本工作中，我们提出了一种新颖的基于深度学习的时空降尺度方法，该方法使用生成对抗网络（GANs），从欧洲中期天气预报中心再分析第五版数据（ERA5）生成历史上准确的高分辨率风资源数据。与以往使用粗化后的高分辨率数据作为低分辨率训练数据的方法不同，我们使用真实的低分辨率模拟输出。我们证明，通过以ERA5作为低分辨率输入、以Wind Integration National Dataset Toolkit（WTK）数据作为高分辨率目标来训练GAN模型，我们在历史准确性和时空变率方面取得了与传统动力降尺度方法相当的结果。这种基于GAN的降尺度方法还将计算成本比动力降尺度降低了两个数量级。我们将该方法应用于将乌克兰、摩尔多瓦和罗马尼亚部分地区2000年1月至2023年12月期间30 km、逐小时的ERA5数据降尺度为多个轮毂高度的2 km、5分钟风数据。这一24年数据记录是基于再分析数据风场的可再生能源资源数据超分辨率数据集（Sup3rWind）的第一个成员。
英文摘要：
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).
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