Abstract: 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. 

Key words: variational mode decomposition ( VMD), convolutional network, long and short term memory network, attention mechanism, short-term load forecasting