基于VMD的CNN-BiLSTM-Att的短期负荷预测

Short-Term Load Prediction of CNN-BiLSTM-Att Based on VMD

为提高短期电力负荷预测精度,提出了基于变分模态分解(VMD:Variational Mode Decomposition)的CNN-BiLSTM-Att(Convolutional Neural Network-Bidirectional Long Short-Term Memory-Attention)的短期负荷预测模型。该模型将历史的负荷数据使用VMD分解成多个子序列负荷并结合天气、日期、工作日类型等因素作为输入特征,得到各个子序列负荷的预测值,然后相加重构组成实际负荷预测曲线。通过与其他模型实验对比,VMD-CNN-BiLSTM-Att模型在测试集上相比于其他模型均有所降低,在连续的周负荷预测中,日负荷预测的平均绝对百分比误差基本维持在1%~2%之间。在复杂负荷变化的非工作日中,平均绝对百分比误差相比CNN-LSTM降低0.13%。证明VMD-CNN-BiLSTM-Att短期负荷预测模型能提高电力负荷预测的精度。

In order to improve the accuracy of short-term power load prediction,a CNN-BiLSTM-Att(Convolutional Neural Network-Bidirectional Long Short-Term Memory-Attention) short-term load prediction model based on variational mode decomposition VMD(Variational Mode Decomposition) is proposed.In this model,the historical load data is decomposed into multiple sub-sequence loads using VMD and combined with weather,date,type of working day and other factors as input characteristics.The predicted value of each sub-sequence load is predicted by this model,and then added and reconstructed to form the actual load prediction curve.By comparison with other models,the VMD-CNN-BiLSTM-Att model has a decrease in the test set.In the continuous weekly load prediction,the average absolute percentage error of daily load prediction is basically maintained between 1%~2%.In the non-working days with complex load changes,the mean absolute percentage error is reduced by 0.13% compared with the CNN-LSTM model.It is proved that VMD-CNN-BiLSTM-Att short-term load forecasting model can improve the accuracy of power load forecasting.