Short-term Electricity Price Prediction Based on CEEMD-TCN-ATTENTION

Abstract

The accuracy of short-term electricity price forecasting has profound influence on the business decisions made by the participants in the electricity market. Thus, we propose a short-term electricity price combination prediction model based on Complete Ensemble Empirical Mode Decomposition(CEEMD) as well as Temporal Convolutional Attention-based Network(TCAN). First, we defined the correlations among the features according to the Pearson Correlation Analysis and selected the desired features for our model. We then defined the single prediction period to be 24 hours based on the Unit Root test. In addition, we decomposed and denoised the data based on CEEMD and built the attention mechanism into the temporal convolution network. Using the expansion cascade network to extract the relationship between the inputs, combined with the self-attention mechanism to extract the internal information and learn the position dependence, effectively improve the model training speed and prediction accuracy. In order to verify the validity of the model, the actual electricity price data of DK1 in the Nordic electricity market from April to May 2020 were forecasted. The results show that the proposed method can effectively improve the accuracy and reliability of electricity price forecasting, and can provide reliable decision-making basis.