用稀疏观测实时重建风机高度风场，误差比传统方法降低66%。
Zhinv: Real-time hub-height wind field reconstruction using only local sparse observations
- 端到端框架直接将稀疏观测编织成精细网格风场。
- 在三地区实验中误差减少约66%，优于克里金插值。
- 适合风电中心直接基于本地数据做实时风资源评估。
大量风电接入电网对区域风场的精细化认知提出更高要求。实际运行中可获取的风信息多为稀疏、离散且分布不规则的局部观测，难以满足风电调控、风资源评估及低空环境感知等任务对连续区域风场的需求。为此，我们提出Zhinv，一种端到端重建框架，可直接将稀疏不规则观测编织成风机高度的细粒度风场。在东北亚、欧洲和东南亚的实验表明，Zhinv能准确、鲁棒且高效地从稀疏观测中重构细网格风场，相比克里金插值（Kriging）误差降低约66%。以本地风电观测为输入，Zhinv使风电中心无需依赖数值天气预报（NWP）和复杂同化流程，支持基于本地数据的直接、实时风资源评估。
原文摘要 · Abstract (English)
The high proportion of wind power connected to the grid places higher demands on fine-grained knowledge of regional wind fields. Since the wind information directly obtainable in actual operations is mostly sparse, discrete, and irregularly distributed local observations, it is difficult to directly meet the needs of tasks such as wind power regulation, wind resource assessment, and low-altitude environmental perception of continuous regional wind fields. Therefore, we propose Zhinv, an end-to-end reconstruction framework that directly weaves sparse and irregular observations into a fine-grid wind field at hub-height. Experiments in Northeast China, Europe, and Southeast Asia demonstrate that Zhinv can accurately, robustly, and efficiently reconstruct fine-grid wind fields from sparse observations, reducing the error by about 66% compared with Kriging. With local wind-power observations as input, Zhinv enables wind power centers to bypass NWP and complex assimilation processes, supporting direct and real-time wind resource assessment from locally available data.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。