WaveHiTS通过小波与分层结构提升风向短时预报精度，误差降低近七成。
WaveHiTS: Wavelet-Enhanced Hierarchical Time Series Modeling for Wind Direction Nowcasting in Eastern Inner Mongolia
- 将风向拆解为U-V分量，结合小波变换捕捉多尺度频率特征
- 在60分钟内预测误差仅19.2°-19.4°，远低于传统模型的56°-64°
- 适合风电场实时控制与电网调度，尤其关注风向高精度预报场景
风向预测对优化风能发电至关重要，但受方向数据的周期性、多步预测中的误差累积及复杂气象交互影响，挑战重重。本文提出新模型WaveHiTS，融合小波变换与时间序列神经分层插值，解决上述问题。方法将风向分解为U-V分量，应用小波变换捕获多尺度频率模式，并采用分层结构建模多尺度时间依赖性，有效缓解误差传播。基于中国内蒙古真实气象数据的实验表明，WaveHiTS显著优于深度学习模型（RNN、LSTM、GRU）、Transformer类模型（TFT、Informer、iTransformer）及混合模型（EMD-LSTM）。其预测均方根误差（RMSE）约为19.2°–19.4°，相较深度学习循环模型的56°–64°大幅降低，在60分钟内保持一致精度。此外，向量相关系数（VCC）达0.985–0.987，命中率88.5%–90.1%，显著超越基线模型。消融实验证明，小波变换、分层结构与U-V分解均对性能有实质性贡献。该改进对提升风机偏航控制效率与风电并网具有重要意义。
原文摘要 · Abstract (English)
Wind direction forecasting plays a crucial role in optimizing wind energy production, but faces significant challenges due to the circular nature of directional data, error accumulation in multi-step forecasting, and complex meteorological interactions. This paper presents a novel model, WaveHiTS, which integrates wavelet transform with Neural Hierarchical Interpolation for Time Series to address these challenges. Our approach decomposes wind direction into U-V components, applies wavelet transform to capture multi-scale frequency patterns, and utilizes a hierarchical structure to model temporal dependencies at multiple scales, effectively mitigating error propagation. Experiments conducted on real-world meteorological data from Inner Mongolia, China demonstrate that WaveHiTS significantly outperforms deep learning models (RNN, LSTM, GRU), transformer-based approaches (TFT, Informer, iTransformer), and hybrid models (EMD-LSTM). The proposed model achieves RMSE values of approximately 19.2°-19.4° compared to 56°-64° for deep learning recurrent models, maintaining consistent accuracy across all forecasting steps up to 60 minutes ahead. Moreover, WaveHiTS demonstrates superior robustness with vector correlation coefficients (VCC) of 0.985-0.987 and hit rates of 88.5%-90.1%, substantially outperforming baseline models. Ablation studies confirm that each component-wavelet transform, hierarchical structure, and U-V decomposition-contributes meaningfully to overall performance. These improvements in wind direction nowcasting have significant implications for enhancing wind turbine yaw control efficiency and grid integration of wind energy.
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