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

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.

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.