为提高短期风速的预测精度,提出一种基于双模式分解、双通道卷积神经网络（CNN）和长短期记忆神经网络（LSTM）的组合预测模型以提高预测精度。首先,对经过PAM方法聚类后的风速时间序列利用奇异谱分解（SSD）和变分模态分解（VMD）2种信号分解方法进行分解,获得2类多尺度分量。不同模式的多尺度分量可降低原始风速的复杂度和非平稳性,实现不同模式模态分量规律的互补;其次,将2种分解方法得到的风速子序列合并为一个矩阵,输入到双通道CNN进行波形特征深度提取;最后,采用LSTM建立历史风速时序的时间依赖关系,在时空相关性分析的基础上得到最终风速预测结果。实验结果表明,基于双模式分解-双通道CNN-LSTM的组合预测模型可有效提高风速短期预测的精度。
Abstract
This paper proposes a short-term wind power combination forecasting method based on two-mode decomposition,two-channel convolutional neural network (CNN) and long short-term memory neural network (LSTM) to improve the forecasting accuracy. Firstly, the wind speed time series clustered by the PAM method is decomposed into two modes of multi-scale subsequences by the singular spectral decomposition (SSD) and variational mode decomposition (VMD). The multi-scale subsequences of different modes can reduce the complexity and nonstationay of the original wind speed,and achieve the complementarity of the two modes.Secondly,the wind speed subsequences obtained by the two decomposition methods are combined into a matrix,which is input to the dual-channel CNN for waveform feature depth extraction. Finally,LSTM network is used to establish the historical wind speed time series dependency relationship,and the final wind speed prediction result is obtained on the basis of spatiotemporal correlation analysis. Experiment results show that the combined prediction model based on dual mode decomposition-dual channel CNN-LSTM can effectively improve the accuracy of short-term wind speed forecasting.
关键词
风力发电 /
深度学习 /
卷积神经网络 /
长短期记忆网络 /
奇异谱分解 /
变分模态分解 /
风速预测
Key words
wind power /
deep learning /
convolutional neural networks /
long short-term memory /
singular spectrum decomposition /
variational mode decomposition /
wind speed forecasting
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