# Short-Term Power Load Forecasting Based on an EPT-VMD-TCN-TPA Model
###### Abstract
Due to the combination of multiple factors, the variation of the load series has high uncertainty and complexity, which makes the traditional statistical model prediction unsatisfactory. In recent years, with the improvement of computer performance, the forecasting methods of artificial intelligence models have been applied to nonlinear time series load forecasting problems. Short-term load forecasting methods based on artificial intelligence mainly include machine learning and deep learning models. Through machine learning techniques, we can learn and mine the non-linear relationship between the independent and dependent variables, and build regression models to predict short-term loads. Some common algorithms, such as support vector regression (SVR) [13], back propagation neuron networks (BPNNs) [14], and artificial neural networks (ANNs) [15], have been widely used for load forecasting. In this paper, we propose a novel model-based model  
forecasting. In Ref. [13], the authors implemented a generic short-term load forecasting strategy based on support vector machines by improving the generation process in the SVR input model and selecting the hyperparameters of the SVR model based on a particle swarm optimization algorithm, which was tested on two publicly available datasets to verify its effectiveness. In Ref. [14], the authors proposed a short-term load forecasting method based on a hybrid optimization algorithm and a BPNN for the load forecasting of paper enterprises, and the experimental results showed that the forecasting results of the forecasting model using the hybrid optimization algorithm outperformed the forecasting results of the single model. Although machine learning methods can handle nonlinear data well, these methods cannot effectively extract data features of load sequences due to the complexity and time series nature of load data. As deep learning continues to evolve, it becomes the focal point of load forecasting. In recent years, convolutional neural networks (CNNs), deep belief networks (DBNs), and deep neural networks (DNNs) have achieved remarkable results in the field of short-term load forecasting [16; 17; 18]. Although these methods are effective in extracting features of multidimensional load data and thus improving prediction precision, they cannot capture the time series features of load sequences when used alone. A recurrent neural network (RNN) is a neural network based on gate structure control, which is able to adjust the information based on past timestamps and current input values, overcoming the problem that ANN models do not handle temporal load data well [19]. A RNN can adversely affect prediction accuracy when dealing with complex nonlinear data due to the possibility of gradient explosion and gradient disappearance, thus limiting its application. The long short-term memory (LSTM) [20] network is an improved version of the RNN, which effectively solves the problem of gradient disappearance and gradient explosion that may occur when the RNN processes complex data by providing memory function and gate structure. The model has been widely used for short-term load forecasting [21; 22]. [PERSON] et al. conducted experiments using a framework based on an LSTM recurrent neural network on a real dataset and finally achieved better prediction results [23]. However, it is difficult to select parameters for the LSTM network as the training data increases. In Ref. [24], the authors first preprocess the load data and predict the load through the established gated recursive unit (GRU) network. The GRU greatly reduces the number of network parameters by merging the forgetting and input gates of the LSTM, which greatly improves the performance of the neural network and makes it easier to achieve convergence.