In the past decades, clean and renewable energy has gained increasing attention due to a global effort on carbon footprint reduction. In particular, Saudi Arabia is gradually shifting its energy portfolio from an exclusive use of oil to a reliance on renewable energy, and, in particular, wind. Modeling wind for assessing potential energy output in a country as large, geographically diverse and understudied as Saudi Arabia is a challenge which implies highly non-linear dynamic structures in both space and time. To address this, we propose a spatio-temporal model whose spatial information is first reduced via an energy distance-based approach and then its dynamical behavior is informed by a sparse and stochastic recurrent neural network (Echo State Network). Finally, the full spatial data is reconstructed by means of a non-stationary stochastic partial differential equation-based approach. Our model can capture the fine scale wind structure and produce more accurate forecasts of both wind speed and energy in lead times of interest for energy grid management and save annually as much as one million dollar against the closest competitive model.

翻译：过去数十年间，由于全球范围内减少碳足迹的努力，清洁可再生能源日益受到关注。特别是沙特阿拉伯正逐步将其能源结构从完全依赖石油转向可再生能源，尤其是风能。在沙特阿拉伯这样一个幅员辽阔、地理多样且研究不足的国家，为评估潜在能源输出而建立风场模型是一项重大挑战，这涉及空间与时间上高度非线性的动态结构。为解决此问题，我们提出一种时空模型：首先通过基于能量距离的方法对空间信息进行降维，随后利用稀疏随机循环神经网络（回声状态网络）刻画其动态行为，最终借助基于非平稳随机偏微分方程的方法重建完整空间数据。该模型能够捕捉精细尺度的风场结构，在电网管理关注的时间尺度内，对风速与风能作出更精准的预测，相比最接近的竞争模型每年可节省高达一百万美元的成本。