DeepMIDE联合预测海上风电多时空多高度风速，提升预报精度。
DeepMIDE: A Multi-Output Spatio-Temporal Method for Ultra-Scale Offshore Wind Energy Forecasting
- 基于多输出积分差分方程，融合物理风场传播机制建模。
- 在美东北部海域数据上，风速与功率预测优于主流方法。
- 适合大规模海上风电场的高精度动态调度与能源规划。
为获取更强风能，海上风电正向更大更髙的风机发展，这要求突破传统单高度风速预测方法。本文提出DeepMIDE——一种联合建模海上风速在空间、时间与高度维度的统计深度学习方法。该方法基于多输出积分差分方程，采用由平流矢量表征的多变量非平稳核函数，捕捉风场形成与传播的物理规律。其深度学习架构从高维外生气象数据中学习这些平流矢量，并将结果回填至统计模型，实现概率性多高度时空风速预测。在美东北部海域真实数据上的测试表明，DeepMIDE在风速与发电功率预测上均显著优于主流的时间序列、时空及深度学习方法。
原文摘要 · Abstract (English)
To unlock access to stronger winds, the offshore wind industry is advancing towards significantly larger and taller wind turbines. This massive upscaling motivates a departure from wind forecasting methods that traditionally focused on a single representative height. To fill this gap, we propose DeepMIDE--a statistical deep learning method which jointly models the offshore wind speeds across space, time, and height. DeepMIDE is formulated as a multi-output integro-difference equation model with a multivariate nonstationary kernel characterized by a set of advection vectors that encode the physics of wind field formation and propagation. Embedded within DeepMIDE, an advanced deep learning architecture learns these advection vectors from high-dimensional streams of exogenous weather information, which, along with other parameters, are plugged back into the statistical model for probabilistic multi-height space-time forecasting. Tested on real-world data from offshore wind energy areas in the Northeastern United States, the wind speed and power forecasts from DeepMIDE are shown to outperform those from prevalent time series, spatio-temporal, and deep learning methods.
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