用于从稀疏气象数据中估计未监测站点风力机高度风速的两步时空框架
A Two-Step Spatio-Temporal Framework for Turbine-Height Wind Estimation at Unmonitored Sites from Sparse Meteorological Data
AI总结：
本文提出了一种两步时空框架，利用稀疏气象数据实时估计风电场风速，提升风能资源评估精度。
摘要：
准确估计风力机 hub 高度的风速对于风能资源评估和具有高可再生能源渗透率的电力电网的日常管理至关重要。在没有直接测量的情况下，参数模型常用于从观测高度推导出风力机高度的风速。最近的文献提出了扩展方法，以允许空间或时间变化的垂直风梯度，即风速随高度变化的速率。然而，这些方法通常假设参考高度和 hub 高度测量在同一地点可用，这限制了其在操作环境中应用，因为气象站和风电场在空间上是分离的。本文开发了一种两步时空框架，仅使用开放访问的稀疏气象站观测数据来估计风力机高度的风速。首先，使用再分析数据训练非参数广义加性模型进行垂直高度推导。其次，空间高斯过程模型插值这些 hub 高度估计值到风电场位置，同时明确传播从高度推导阶段的不确定性。所提出的框架能够利用实时可用的数据构建高分辨率、亚小时级的风力机高度风速时间序列和空间风图，这些能力是现有再分析产品所不具备的。我们进一步提供校准的不确定性估计，以考虑垂直推导和空间插值误差。该方法通过爱尔兰七个运营风电场的 hub 高度测量进行验证，证明了其比 ERA5 再分析更准确，同时仅依赖于实时、开放获取的数据。
英文摘要：
Accurate estimates of wind speeds at wind turbine hub heights are crucial for both wind resource assessment and day-to-day management of electricity grids with high renewable penetration. In the absence of direct measurements, parametric models are commonly used to extrapolate wind speeds from observed heights to turbine heights. Recent literature has proposed extensions to allow for spatially or temporally varying vertical wind gradients, that is, the rate at which wind speed changes with height. However, these approaches typically assume that reference height and hub height measurements are available at the same locations, which limits their applicability in operational settings where meteorological stations and wind farms are spatially separated. In this paper, we develop a two-step spatio-temporal framework to estimate turbine height wind speeds using only open-access observations from sparse meteorological stations. First, a non-parametric generalized additive model is trained on reanalysis data to perform vertical height extrapolation. Second, a spatial Gaussian process model interpolates these hub-height estimates to wind farm locations while explicitly propagating uncertainty from the height extrapolation stage. The proposed framework enables the construction of high-resolution, sub-hourly turbine-height wind speed time series and spatial wind maps using data available in real time, capabilities not provided by existing reanalysis products. We further provide calibrated uncertainty estimates that account for both vertical extrapolation and spatial interpolation errors. The approach is validated using hub-height measurements from seven operational wind farms in Ireland, demonstrating improved accuracy relative to ERA5 reanalysis while relying solely on real-time, open-access data.
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