WiSoSuper：风能和太阳能数据的基准超分辨率方法
WiSoSuper: Benchmarking Super-Resolution Methods on Wind and Solar Data
摘要：
向绿色能源电网的过渡取决于详细的风能和太阳能预测，以优化可再生能源发电的选址和调度。然而，数值天气预报模型的业务预报只有10至20公里的空间分辨率，这导致可再生能源农场的使用和发展不够理想。气象科学家一直在开发超分辨率方法来提高分辨率，但通常依赖于简单的插值技术或计算昂贵的基于微分方程的模型。最近，基于机器学习的模型，特别是物理信息的分辨率增强生成对抗网络（PhIREGAN），已经优于传统的降尺度方法。我们提供了领先的基于深度学习的超分辨率技术的全面和可扩展的基准，包括增强型超分辨率生成对抗网络（ESRGAN）和增强型深度超分辨率（EDSR）网络，用于风能和太阳能数据。我们伴随着一个新的公共，处理，机器学习准备数据集的基准测试超分辨率方法对风能和太阳能数据。
英文摘要：
The transition to green energy grids depends on detailed wind and solar forecasts to optimize the siting and scheduling of renewable energy generation. Operational forecasts from numerical weather prediction models, however, only have a spatial resolution of 10 to 20-km, which leads to sub-optimal usage and development of renewable energy farms. Weather scientists have been developing super-resolution methods to increase the resolution, but often rely on simple interpolation techniques or computationally expensive differential equation-based models. Recently, machine learning-based models, specifically the physics-informed resolution-enhancing generative adversarial network (PhIREGAN), have outperformed traditional downscaling methods. We provide a thorough and extensible benchmark of leading deep learning-based super-resolution techniques, including the enhanced super-resolution generative adversarial network (ESRGAN) and an enhanced deep super-resolution (EDSR) network, on wind and solar data. We accompany the benchmark with a novel public, processed, and machine learning-ready dataset for benchmarking super-resolution methods on wind and solar data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
