用深度回声网络与随机微分方程建模沙特高分辨率风场，提升风电预测精度。
Modeling High-Resolution Spatio-Temporal Wind with Deep Echo State Networks and Stochastic Partial Differential Equations
- 先用能量距离降维空间信息，再用稀疏随机循环网络捕捉时空动态。
- 在沙特全境实现高精度风速与发电量预测，领先竞品年省百万美元。
- 适合能源规划、电网调度及可再生能源建模研究者参考。
近年来，为减少碳足迹，清洁能源日益受到关注。沙特阿拉伯正从依赖石油转向发展可再生能源，尤其是风能。由于沙特国土广阔、地理多样且研究不足，其风场建模面临巨大挑战，时空非线性结构复杂。为此，本文提出一种时空模型：首先通过基于能量距离的方法降低空间维度，再利用稀疏随机循环神经网络（回声状态网络）刻画动态行为，最后通过非平稳随机偏微分方程重建完整空间数据。该模型能有效捕捉细粒度风场结构，在风电预测中表现优异，对电网管理所需预测时长内，风速与发电量预测精度显著提升，相较最优竞争模型每年可节省约一百万美元。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。