织女座：仅使用局部稀疏观测值进行实时轮毂高度风场重建
Zhinv: Real-time hub-height wind field reconstruction using only local sparse observations
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中文总结 AI 辅助
针对实际运行中风信息多为稀疏离散的问题，提出织女座端到端重建框架，能将稀疏观测编织成轮毂高度风场。实验表明其可准确高效重建风场，相比克里金法误差降低约66%，支持实时风资源评估。
中文摘要 AI 辅助
风电并网比例的提高对区域风场的细粒度知识提出了更高要求。由于实际运行中可直接获取的风信息大多是稀疏、离散且分布不规则的局部观测值，难以直接满足风电调节、风资源评估和连续区域风场低空环境感知等任务的需求。因此，我们提出了织女座，这是一个端到端的重建框架，可将稀疏且不规则的观测值直接编织成轮毂高度处的精细网格风场。在中国东北、欧洲和东南亚的实验表明，织女座能够从稀疏观测值中准确、稳健且高效地重建精细网格风场，与克里金法相比误差降低约66%。以局部风电观测值为输入，织女座使风电中心能够绕过数值天气预报和复杂的同化过程，支持从本地可用数据进行直接和实时的风资源评估。
英文摘要
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.
