融合时频特征的混合模型，显著提升海浪高度预测精度。
STL-FFT-STFT-TCN-LSTM: An Effective Wave Height High Accuracy Prediction Model Fusing Time-Frequency Domain Features
- 结合STL、FFT、STFT与TCN-LSTM，多尺度捕捉波浪特征。
- 相较其他模型，MAE降低15.8%~40.5%，R值提升1.31%~2.9%。
- 适合海洋能源开发中高精度波浪预测，尤其擅长极端波高捕捉。
随着传统能源消耗加剧及其环境负面影响凸显，波浪能因其高能量密度、稳定性、广泛分布及环境友好性，成为可再生能源中的重要成员。其发展关键在于准确预测有效波高（WVHT）。然而，波浪信号具有强非线性、突变、多尺度周期性、数据稀疏及高频噪声干扰等特性；且物理模型计算成本极高。为此，本文提出一种融合STL-FFT-STFT-TCN-LSTM的混合模型，利用季节趋势分解（STL）、快速傅里叶变换（FFT）、短时傅里叶变换（STFT）、时序卷积网络（TCN）与长短期记忆网络（LSTM），优化多尺度特征融合，有效捕捉极端波高并抑制高频噪声。实验基于2019至2022年NOAA站41008和41047的小时级数据，结果表明，该模型在极端波高预测与噪声抑制方面显著优于单一及混合模型，MAE降低15.8%–40.5%，SMAPE降低8.3%–20.3%，R值提升1.31%–2.9%；消融实验验证各组件不可或缺，证实其在多尺度特征融合上的优势。
原文摘要 · Abstract (English)
As the consumption of traditional energy sources intensifies and their adverse environmental impacts become more pronounced, wave energy stands out as a highly promising member of the renewable energy family due to its high energy density, stability, widespread distribution, and environmental friendliness. The key to its development lies in the precise prediction of Significant Wave Height (WVHT). However, wave energy signals exhibit strong nonlinearity, abrupt changes, multi-scale periodicity, data sparsity, and high-frequency noise interference; additionally, physical models for wave energy prediction incur extremely high computational costs. To address these challenges, this study proposes a hybrid model combining STL-FFT-STFT-TCN-LSTM. This model exploits the Seasonal-Trend Decomposition Procedure based on Loess (STL), Fast Fourier Transform (FFT), Short-Time Fourier Transform (STFT), Temporal Convolutional Network (TCN), and Long Short-Term Memory (LSTM) technologies. The model aims to optimize multi-scale feature fusion, capture extreme wave heights, and address issues related to high-frequency noise and periodic signals, thereby achieving efficient and accurate prediction of significant wave height. Experiments were conducted using hourly data from NOAA Station 41008 and 41047 spanning 2019 to 2022. The results showed that compared with other single models and hybrid models, the STL-FFT-STFT-TCN-LSTM model achieved significantly higher prediction accuracy in capturing extreme wave heights and suppressing high-frequency noise, with MAE reduced by 15.8\%-40.5\%, SMAPE reduced by 8.3\%-20.3\%, and R increased by 1.31\%-2.9\%; in ablation experiments, the model also demonstrated the indispensability of each component step, validating its superiority in multi-scale feature fusion.
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