# Short-Term Power Load Forecasting Based on an EPT-VMD-TCN-TPA Model
###### Abstract
In this method, a CNN is used to extract features in load sequences, BILSTM can learn both forward and backward data features, and a GWO is used for parameter search optimization of the CNN and BILSTM networks, and finally, four different cases are analyzed to verify the effectiveness of the proposed method. A nice combinatorial model has also been proposed in Ref. [43]. Considering the volatility, nonlinearity, and time series of load series, this paper proposes a short-term load forecasting method based on the combination of EPT-VMD hybrid decomposition and the TCN-TPA network model, called EPT-VMD-TCN-TPA. Compared with other methods, the EPT method used in this paper before using VMD will extract the trend component of the load series, while the VMD method will further reduce the volatility of the remaining fluctuation components. The TCN model based on the temporal attention mechanism used in this paper not only focuses more on the temporal characteristics but also the TCN model is suitable for the prediction of large data volume. First, the trend component (Tr(t)) and the residual fluctuation component (Re(t)) of the load series are extracted using the EPT method. The residual fluctuation component (Re(t)) is then adaptively decomposed into intrinsic modal functions (IMFs) with different central frequencies using the VMD method. Finally, the Tr(t) component, as well as the IMF component, are combined with meteorological influence factors, respectively, and the TCN-TPA network is used to train and predict the electric load. We also discuss the complexity of using fuzzy entropy (FE) to calculate the components of IMFs, and the intrinsic modal function (IMF) components with similar complexity are reorganized into  
Figure 2: The general flow of power load forecasting. a new component (FE-IMF), which is called the EPT-VMD-FE-TPA-TCN load prediction model. Simulation modeling was conducted using two regional load datasets provided by the 9 th National Student Electrician Mathematical Modeling Competition in China, and the results show that the proposed EPT-VMD-TCN-TPA short-term electric load forecasting model predicts better precision than other comparative models and has better applicability. The main contributions of this paper are further elucidated as follows:  
1. Since the load series has a certain fundamental trend, the fundamental trend dominates the direction of the load series over time. The established EPT can accurately extract the fundamental trend of the load series, thus improving the accuracy of short-term load forecasting. 2. The adopted VMD decomposes the residual fluctuation series into several subseries with different center frequencies, which further reduces the non-smoothness of the fluctuation series. The number of decompositions of the VMD is determined using the ratio of residual energy, which makes the decomposed subsequences smoother. 3. The experimental results show that the EPT-VMD hybrid decomposition method proposed in this paper is effective for load prediction. 4. The TCN-TPA network is used for the prediction of individual components so that the temporal characteristics of these sequences can be better extracted. The temporal pattern attention mechanism incorporated in the TCN network can better assign different weights to different variables at the same time step compared to the ordinary attention mechanism. The temporal pattern attention mechanism enhances the impact of temporal attributes of multivariate load sequences on load prediction. 5. In further experiments, FE was used to analyze the complexity of the IMF components and to reconstruct these components using similar entropy values. The experimental results show that the EPT-VMD-FE-TCN-TPA reconstructed model using FE has higher operational efficiency compared to the EPT-VMD-TCN-TPA prediction model, but the prediction precision is reduced. The rest of the paper is organized as follows. Section 2 presents the relevant methods. Section 3 explores the proposed model.