# Short-Term Power Load Forecasting Based on an EPT-VMD-TCN-TPA Model
###### Abstract
Another approach is to use signal decomposition methods to decompose the load data sequence into several relatively smooth sequences, and then predict each component separately. These signal decomposition methods are mainly wavelet transforms, empirical modal decomposition (EMD), and improved methods based on EMD, etc. In Ref. [33], the authors propose a prediction method based on a combination of wavelet decomposition and second-order gray neural networks. In Ref. [34], the authors first decompose the load sequence using EMD, followed by modeling prediction using a BiLSTM network based on an attention mechanism. In Ref. [35], the authors first decompose the load sequences using an ensemble empirical modal decomposition (EEMD), followed by reconstructing enough new sequences based on the approximate entropy of each component, and finally use an extreme learning machine (ELM) to predict each sequence separately and superimpose their results. Although these decomposition methods make load forecasting more accurate to a certain extent, they also have certain problems. For example, the effect of wavelet transformation is related to the choice of wavelet basis, which is less applicable. EMD decomposition tends to lead to modal confusion and endpoint effects when there are significant step changes in the signal. The EEMD decomposition reduces the residual auxiliary noise and reconstruction errors during EMD decomposition by adding Gaussian white noise several times, thus reducing the generation of modal aliasing. In recent years, variational modal decomposition (VMD) has been widely used in the field of short-term power load forecasting [36; 37]. With the VMD decomposition technique, [PERSON] et al. divided the load sequence into more stable parts and used a predictive network model to make accurate predictions for these parts [38]. The experimental results show that the VMD decomposition technique can significantly improve the accuracy and precision of load prediction. VMD is fundamentally different from EMD in that it uses a non-recursive approach to achieve signal decomposition and can automatically adjust the number of decompositions according to the actual situation, thus effectively separating the intrinsic modal components and also effectively solving the problems of modal aliasing and endpoint effects. Recently, an ensemble patch transform (EPT) for signal decomposition and filtering has been proposed by [PERSON] et al. [39]. This EPT method can effectively decompose complex signals by specific transformations and can extract features such as trends of non-smooth series. In the short-term load forecasting model proposed in this paper, we use a hybrid decomposition technique based on EPT and VMD to effectively decompose and extract the features of the load sequence. In summary, a combined forecasting model based on optimization algorithms, attention mechanisms, and various forecasting methods can be found throughout, as seen in the general process of electric load forecasting shown in Figure 2. In Ref. [40], the authors used a combined approach of neural networks and particle swarm optimization algorithms to forecast load, which was tested on data from a North American electric utility, and acceptable accuracy was obtained. In Ref. [41], the authors developed a novel multi-dual forecasting system for electricity prices using multivariate and multi-input-multi-output structures. In this model, the data obtained from pre-processing are predicted by a back propagation network, a BiLSTM network, and a GRU network, and finally, the final prediction results are obtained based on the combined strategy of the salp swarm algorithm, and the experimental results show that the proposed method achieves good prediction results. In Ref. [42], the authors propose a hybrid GWO-CNN-BILSTM prediction model for the short-term load prediction of buildings.