用输入变形高斯过程建模风电预测的时空概率特性。
Probabilistic Spatiotemporal Modeling of Day-Ahead Wind Power Generation with Input-Warped Gaussian Processes
- 设计可分离的时空核，结合时间与空间输入变形。
- 在德州ERCOT区域数据上验证模型能有效捕捉非平稳性。
- 适合电力系统规划与风电并网研究者参考。
本文构建了一个高斯过程（GP）时空模型，用于刻画小时级日前风电预测的特征。针对数百个风电场的小时尺度预测数据，目标是建立一个跨空间与时段的完整概率联合模型。为此，设计了一种可分离的时空核，通过时间和空间的输入变形来捕捉风电功率协方差的非平稳性。通过模拟实验验证了空间核的选择，并展示了变形对处理非平稳性的有效性。后半部分基于德克萨斯州ERCOT区域的真实校准数据集进行了详细案例研究。
原文摘要 · Abstract (English)
We design a Gaussian Process (GP) spatiotemporal model to capture features of day-ahead wind power forecasts. We work with hourly-scale day-ahead forecasts across hundreds of wind farm locations, with the main aim of constructing a fully probabilistic joint model across space and hours of the day. To this end, we design a separable space-time kernel, implementing both temporal and spatial input warping to capture the non-stationarity in the covariance of wind power. We conduct synthetic experiments to validate our choice of the spatial kernel and to demonstrate the effectiveness of warping in addressing nonstationarity. The second half of the paper is devoted to a detailed case study using a realistic, fully calibrated dataset representing wind farms in the ERCOT region of Texas.
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