Zhinv: Real-time hub-height wind field reconstruction using only local sparse observations

Abstract: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.

Machine Learning (cs.LG)

arXiv:2607.25298 [cs.LG]

(or arXiv:2607.25298v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2607.25298 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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