CNN-BiLSTM Day-ahead Electricity Price Prediction Based on Attention Mechanism
The accuracy of the day-ahead electricity price prediction results is of great importance to the electricity market with diversified competition situation.In this paper,we propose a CNN-BiLSTM day-ahead electricity price prediction model based on the attention mechanism.The model considers the influence of many factors on day-ahead price prediction,adopts Pearson coefficient for correlation analysis to obtain the influence of each factor,uses convolutional neural network to extract the features in the sequence of historical electricity price,inputs the extracted feature information into bi-directional long-and short-term memory network,and fully utilizes the change rule of the features for training.The attention mechanism is then introduced to highlight the influence of important information and assigns the weights,and the final prediction value is calculated by weighted sum of activation functions in the fully connected layer.The accuracy of the proposed model is verified by examples,in which the RMSE,MAPE and MAE are reduced by 33.07%,28.39%and 27.08％,respectively.
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