An accurate and timely assessment of wind speed and energy output allows an efficient planning and management of this resource on the power grid. Wind energy, especially at high resolution, calls for the development of nonlinear statistical models able to capture complex dependencies in space and time. This work introduces a Convolutional Echo State AutoencodeR (CESAR), a spatio-temporal, neural network-based model which first extracts the spatial features with a deep convolutional autoencoder, and then models their dynamics with an echo state network. We also propose a two-step approach to also allow for computationally affordable inference, while also performing uncertainty quantification. We focus on a high-resolution simulation in Riyadh (Saudi Arabia), an area where wind farm planning is currently ongoing, and show how CESAR is able to provide improved forecasting of wind speed and power for proposed building sites by up to 17% against the best alternative methods.

翻译：准确及时地评估风速与能量输出，有助于在电网中对此类资源进行高效规划与管理。风能（尤其是高分辨率风能）的预测，要求开发能够捕捉时空复杂依赖关系的非线性统计模型。本研究提出一种卷积回声状态自编码器（CESAR），这是一种基于神经网络的时空模型：其首先通过深度卷积自编码器提取空间特征，随后利用回声状态网络对这些特征的动态特性进行建模。我们还提出一种两步计算框架，在实现可负担的计算推理的同时，亦能进行不确定性量化。本研究聚焦于沙特阿拉伯利雅得地区的高分辨率模拟（该区域目前正在进行风电场规划），并展示CESAR如何为拟建场址的风速与功率提供较现有最佳方法提升高达17%的预测精度。