基于两级分解和长短时记忆网络的短期风速多步组合预测模型

Combined Model Based on Two-stage Decomposition and Long-short-term Memory Network for Short-term Wind Speed Multi-step Prediction

  • 摘要: 为了更好地提取与学习风速在时域和频域上的特征,解决风速信号时域随机性和频域复杂性问题,提出了一种基于小波分解(WD)、变分模态分解(VMD)、长短时记忆(LSTM)网络和注意力机制(AT)的短期风速组合预测模型(WD-VMD-DLSTM-AT).在此基础上,提出了一种基于注意力机制的多输入多输出(MIMO)的编码解码多步预测模型(MMED-AT).通过实验对比分析,所提出的组合预测模型具有最优的统计误差,在短期风速预测方面能显著提高预测精度.基于组合模型的(MMED-AT)模型能明显消除递归式多步预测的累积误差,进一步提高多步预测的平稳性.

     

    Abstract: To better extract and study the characteristics of wind speed in the time and frequency domains, and to solve the time-domain randomness and frequency-domain complexity problems of the wind speed signal, we propose a combined short-term prediction model, WD-VMD-DLSTM-AT, which is based on wavelet decomposition and reconstruction (WD), variational mode decomposition (VMD), a long-short-term memory (LSTM) network and an attention mechanism (AT). On this basis, we propose a multi-input multiple output (MIMO) codec multi-step prediction model (MMED-AT) based on an attention mechanism. A comparison and analysis of the experiment results proves that the proposed combined forecasting model has the smallest statistical error, and can significantly improve the prediction accuracy in the short-term wind speed prediction. MMED-AT models based on the proposed combined model can obviously eliminate the cumulative error of recursive multi-step prediction and improve the stability of multi-step prediction.

     

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