发表机构
School of Artificial Intelligence, Hubei University; School of Economics and Management, Wuhan University; School of Electrical Engineering, Shanghai Jiao Tong University（湖北大学人工智能学院; 武汉大学经济与管理学院; 上海交通大学电气工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对台风阵风短期预测难题，提出整合平稳小波分解等模块的WDANet框架，以西太平洋近海区域为对象验证，其在24小时预测时域的短提前期预测中精度优于ECMWF-HRES，可应用于海上风电等场景。
AI 中文摘要
台风条件下的阵风精准预测仍是一项挑战，因为极端风波动具有高度非平稳和多尺度特征。现有深度学习模型常难以同时捕捉长期趋势与快速局部变化，导致极端事件期间性能下降。我们提出WDANet，这是一种频率感知预测框架，整合了平稳小波分解、特征线性调制（FiLM）策略及双分支编解码器架构，可分别对趋势与波动分量建模。以中国西太平洋近海区域为例，我们开展了精细网格阵风预测研究。结果显示，在实验设置下，WDANet在24小时预测时域内的短提前期预测中展现优势，且在前6小时内比ECMWF-HRES预测精度更高。在极端风事件期间，WDANet能更精准捕捉阵风峰值，取得最优的RMSE（均方根误差）和MAE（平均绝对误差）性能。这些结果凸显了其在海上风电运营、灾害预警及风险缓解方面的应用潜力。
英文摘要
Accurate gust forecasting under typhoon conditions remains challenging due to the highly non-stationary and multi-scale characteristics of extreme wind fluctuations. Existing deep learning models often struggle to simultaneously capture long-term trends and rapid local variations, resulting in degraded performance during extreme events. We propose WDANet, a frequency-aware forecasting framework that integrates stationary wavelet decomposition, a Feature-wise Linear Modulation (FiLM) strategy, and a dual-branch encoder-decoder architecture, enabling separate modeling of trend and fluctuation components. Taking the offshore regions of the Western Pacific in China as an example, we conduct fine-grid wind gust prediction research. The results demonstrate that WDANet shows advantages for short lead times under the experimental setting across a 24-h forecasting horizon and achieves higher prediction accuracy than ECMWF-HRES within the first 6 h. During extreme wind events, WDANet more accurately captures gust peaks and attains the best RMSE and MAE performance. These results highlight its potential for offshore wind power operation, disaster warning, and risk mitigation.
