Problem
Research questions and friction points this paper is trying to address.
wind field reconstruction
sparse observations
hub-height wind
real-time wind assessment
regional wind fields
Zhinv: Real-time hub-height wind field reconstruction using only local sparse observations
🤖 AI Summary
This study addresses the challenge of reconstructing high-resolution, continuous hub-height wind fields in real time from sparse, discrete, and irregularly distributed local wind observations. To this end, the authors propose Zhinv, an end-to-end deep learning framework that, for the first time, generates high-resolution wind field grids using only local sparse wind measurements—without relying on numerical weather prediction (NWP) models or complex data assimilation. By integrating spatial interpolation with physical constraints, Zhinv effectively handles irregular input distributions and produces physically consistent, continuous wind fields. Experiments across Northeast China, Europe, and Southeast Asia demonstrate that Zhinv reduces reconstruction errors by approximately 66% compared to Kriging, offering superior accuracy, robustness, and real-time performance, thereby significantly enhancing the efficiency and practicality of wind field reconstruction.
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
wind field reconstruction
sparse observations
end-to-end framework
hub-height wind
real-time assessment
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