Multi-Step Ahead Short-Term Electricity Load Forecasting Using VMD-TCN and Error Correction Strategy

Abstract

The electricity load forecasting plays a pivotal role in the operation of power utility companies precise forecasting and is crucial to mitigate the challenges of supply and demand in the smart grid. More recently, the hybrid models combining signal decomposition and artificial neural networks have received popularity due to their applicability to reduce the difficulty of prediction. However, the commonly used decomposition algorithms and recurrent neural network-based models still confront some dilemmas such as boundary effects, time consumption, etc. Therefore, a hybrid prediction model combining variational mode decomposition (VMD), a temporal convolutional network (TCN), and an error correction strategy is proposed. To address the difficulty in determining the decomposition number and penalty factor for VMD decomposition, the idea of weighted permutation entropy is introduced. The decomposition hyperparameters are optimized by using a comprehensive indicator that takes account of the complexity and amplitude of the subsequences. Besides, a temporal convolutional network is adopted to carry out feature extraction and load prediction for each subsequence, with the primary forecasting results obtained by combining the prediction of each TCN model. In order to further improve the accuracy of prediction for the model, an error correction strategy is applied according to the prediction error of the train set. The Global Energy Competition 2014 dataset is employed to demonstrate the effectiveness and practicality of the proposed hybrid model. The experimental results show that the prediction performance of the proposed hybrid model outperforms the contrast models. The accuracy achieves 0.274%, 0.326%, and 0.405 for 6-steps, 12-steps, and 24 steps ahead forecasting, respectively, in terms of the mean absolute percentage error.