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.

翻译：为获取更强风力资源，海上风电产业正朝着显著更大、更高的风力涡轮机方向发展。这种大规模升级促使我们需摒弃传统上仅关注单一代表性高度的风能预测方法。为填补这一空白，本文提出DeepMIDE——一种统计深度学习方法，可联合建模海上风速在空间、时间和高度维度的分布。DeepMIDE被构建为多输出积分差分方程模型，其多元非平稳核函数由一组平流向量表征，这些向量编码了风场形成与传播的物理机制。通过嵌入先进的深度学习架构，DeepMIDE能够从高维外源气象数据流中学习这些平流向量，并将其与其他参数共同反馈至统计模型中，实现多高度时空概率预测。基于美国东北部海上风能区的实测数据验证表明，DeepMIDE在风速与功率预测方面均优于主流时间序列方法、时空模型及深度学习基准方法。