基于小波变换与BiGRU-NN模型的短期负荷预测方法

Short-term load forecasting method based on wavelet transform and BiGRU-NN model

为更好地挖掘大量采集数据蕴含的有效信息,提高短期负荷预测精度,文中提出一种基于小波变换与双向门控循环单元(BiGRU)、全连接神经网络(NN)混合模型的短期负荷预测方法。文章利用小波变换将负荷特征数据分解为高频数据以及低频数据,再分别建立高频混合神经网络以及低频混合神经网络模型进行预测。在混合神经网络模型中,将负荷特征数据作为BiGRU-NN网络的输入,利用BiGRU-NN网络学习负荷非线性以及时序性特征,以此进行短期负荷预测。文中以丹麦东部地区的负荷数据作为算例,实验结果表明,该方法与GRU神经网络、DNN神经网络、CNN-LSTM神经网络相比,具有更高的预测精度。

In order to better mine the effective information contained in a large amount of collected data and improve the accuracy of short-term load forecasting,a short-term load forecasting method based on a hybrid model of wavelet transform and bidirectional gated recurrent unit(BiGRU)and fully-connected neural network(NN)is proposed in this paper.The wavelet transform is used to decompose the load characteristic data into high-frequency data and low-frequency data,and then,a high-frequency mixed neural network and a low-frequency mixed neural network model are built respectively to conduct prediction.In the hybrid neural network model,the load characteristic data is used as the input of the BiGRU-NN network,and the BiGRU-NN network is used to learn the load nonlinearity and time series characteristics to perform short-term load prediction.Taking the load data of Eastern Denmark as an example,the experimental results show that the proposed method has higher prediction accuracy than the GRU neural network,DNN neural network,and CNN-LSTM neural network.