发表机构
The Hong Kong University of Science and Technology; The Chinese University of Hong Kong; Tsinghua University; Goldwind Science and Technology Co.,Ltd（香港科技大学; 香港中文大学; 清华大学; 金风科技股份有限公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对风力发电预测难题，提出多模态框架，整合SCADA数据与NWP预测。先分解输入特征，再用几何编码器和傅里叶神经算子建模，实验表明该模型优于现有基线，凸显基于物理设计的有效性。
AI 中文摘要
准确的短期风力发电预测对电网稳定性和运营规划至关重要，但由于大气条件与涡轮机动力学之间的复杂相互作用，这一任务仍具有挑战性。现有方法未能有效整合天气预报与风力涡轮机数据（即SCADA），导致解决方案欠佳。为解决此问题，我们引入了一个多模态框架，将基于历史点的SCADA数据与基于网格的数值天气预报（NWP）预测相结合，这因异构输入和复杂的物理风力涡轮机相互作用而颇具挑战。我们的方法首先将输入明确分解为标量和矢量特征，以更好地捕捉特定地点和几何相关性，然后采用几何编码器从风矢量中提取旋转不变特征。我们还利用了傅里叶神经算子（FNO）架构，它在频域中执行全局卷积，以有效建模远程时空关系。在三个实际风电场进行的广泛实验表明，我们的模型始终优于现有基线，凸显了其基于物理的设计的有效性。我们方法的核心实现可在指定网址公开获取。
英文摘要
Accurate short-term wind power forecasting is essential for grid stability and operational planning, yet remains challenging due to the complex interactions between atmospheric conditions and turbine dynamics. However, existing methods fail to effectively incorporate weather forecasting with wind turbine data (i.e., SCADA), leading to suboptimal solutions. To address this, we introduce a multimodal framework that integrates historical point-based SCADA data with grid-based Numerical Weather Prediction (NWP) forecasts, which is challenging due to heterogeneous input and the complex physical wind-turbine interactions. Our approach first explicitly decomposes inputs into scalar and vector features to better capture both site-specific and geometric dependencies and then incorporates a geometric encoder to extract rotation-invariant features from wind vectors. We further leverages a Fourier Neural Operator (FNO) architecture, which performs global convolutions in the frequency domain to efficiently model long-range spatiotemporal relationships. Extensive experiments on three real-world wind farms, with weather forecasting data, demonstrate that our model consistently outperforms state-of-the-art baselines, highlighting the effectiveness of its physically-informed design. The core implementation of our method is publicly available at: https://github.com/shawn-sypiao/GWPF.
