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This paper introduces Zhinv, an end-to-end reconstruction framework that transforms sparse and irregular local wind observations into a fine-grid wind field at hub-height. By leveraging local wind-power data, Zhinv significantly enhances the accuracy of wind field reconstructions, achieving a reduction in error of approximately 66% compared to traditional Kriging methods. The framework's efficiency allows for real-time wind resource assessments, which is crucial for optimizing wind power regulation and environmental monitoring.
Zhinv slashes wind field reconstruction errors by 66%, enabling real-time assessments directly from local observations.
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.
