# Short-Term Power Load Forecasting Based on an EPT-VMD-TCN-TPA Model
###### Abstract
Considering that the LSTM and GRU can only encode the information of load sequences from front to back and cannot adequately consider the future information, some scholars have used bidirectional long short-term memory (BiLSTM) and bidirectional gated recurrent unit (BiGRU) for load prediction [25; 26]. In Ref. [25], the authors design a BiLSTM forecasting model based on improved particle swarm optimization, which designs a selection mechanism taking into account trend similarity and momentary similarity, thus combining the predicted and historical similar values and improving the forecast precision. In Ref. [26], the authors proposed a CNN-BiGRU method for short-term load prediction in which high-dimensional features and time-dependent features of the load data were extracted using a CNN and BiGRU, respectively, and an attention mechanism was introduced to the BiGRU network, and the results showed that the method would be more effective than the LSTM method. The BiLSTM and BiGRU networks have both forward and backward sequential information inputs, which can fully extract information from the load data and thus may improve the accuracy of feature extraction to some extent. In order to solve the problems of CNN's inability to capture sequence timing features and the lack of using convolutional operations to extract data features in recurrent neural networks, temporal convolutional networks (TCNs) were created. The TCN is a new architecture based on convolutional neural networks that can efficiently extract sequence timing features. The TCN has improved on the basis of the CNN for the time series problem and is able to extract feature information of nonlinear time series data, while the TCN model solves the problem of gradient explosion and gradient disappearance by initializing the weight parameters. For load prediction as a complex nonlinear optimization problem, [PERSON] et al. built a thermal load prediction model based on TCN networks to achieve the extraction of complex data features and accurate load prediction [27]. Due to the advantages of TCN networks, many scholars have used them for short-term load forecasting [28; 29]. Considering the non-linear and fluctuating characteristics of power load data, many experts and scholars pre-process the power load data before forecasting. One approach is to use a suitable method to classify the time series data into deterministic and stochastic components, and then model them separately based on these two different types of components. The deterministic component takes into account the characteristics of the load series such as trend, annual, quarterly, and weekly cycles, and date similarity, while other stochastic influences will determine the stochastic component. In Ref. [30], [PERSON] et al., considering the characteristics such as polar values of electric load data, proposed a function data modeling method that divides the sequence after polar value processing, into deterministic and random components, and uses generalized additive modeling techniques to model the deterministic component and three different models to model the random component. The experimental results show that the prediction error based on the deterministic component and the stochastic component is predicted separately with lower prediction errors than the classical comparison models. Meanwhile, in Ref. [31], [PERSON] et al. first divided the time series (demand and price) into deterministic and stochastic components. The deterministic component is modeled using parametric and nonparametric methods, and the stochastic component is modeled using univariate autoregression (AR) and multivariate vector autoregression (VAR), and finally, the model proposed in the paper is known to have better forecasting ability by evaluating the indicators, and the effectiveness of the model proposed in the paper is further verified by statistical significance. In Ref. [32], the method based on component estimation also achieved better prediction results.