# Short-Term Power Load Forecasting Based on an EPT-VMD-TCN-TPA Model
###### Abstract  
Accurate short-term load forecasting is the key to ensuring smooth and efficient power system operation and power market dispatch planning. However, the nonlinear, non-stationary, and time series nature of load sequences makes load forecasting difficult. To address these problems, this paper proposes a short-term load forecasting method (EPT-VMD-TCN-TPA) based on the hybrid decomposition of load sequences, which combines ensemble patch transform (EPT), variational modal decomposition (VMD), a temporal convolutional network (TCN), and a temporal pattern attention mechanism (TPA). In which, the trend component (Tr(t)) and the residual fluctuation component (Re(t)) of the load series are extracted using EPT, and then the Re(t) component is decomposed into intrinsic modal function components (IMFs) of different frequencies using VMD. The Tr(t) and IMFs components of the fused meteorological data are predicted separately by the TCN-TPA prediction model, and finally, the prediction results of each component are reconstructed and superimposed to obtain the final predicted value of the load. In addition, experiments after reconstructing each IMF component according to the fuzzy entropy (FE) values are discussed in this paper. To evaluate the performance of the proposed method in this paper, we used datasets from two Areas of the 9 th Mathematical Modeling Contest in China. The experimental results show that the predictive precision of the EPT-VMD-TCN-TPA model outperforms other comparative models. More specifically, the experimental results of the EPT-VMD-TCN-TPA method had a MAPE of 1.25% and 1.58% on Area 1 and Area 2 test sets, respectively. short-term load prediction; ensemble patch transformation; variational modal decomposition; temporal convolutional networks; temporal pattern attention; fuzzy entropy +
Footnote †: journal: Applied Sciences  
of power systems rely on ultra-short-term load forecasting. Short-term load forecasting can coordinate the daily start/stop and generation plans of each generating unit, while medium- and long-term load forecasting aims to provide a reference basis for the long-term planning of power systems, etc. [6]. In previous studies, numerous scholars have proposed many methods to improve the accuracy of load forecasting, most of which focus on methods for short-term load forecasting. These methods are divided into three main categories based on statistical models, artificial intelligence prediction models, and combined prediction models, as shown in Figure 1. Multiple linear regression (MLR) [7], exponential smoothing (ES) [8], Kalman filter [9], the Hidden Markov model (HMM) [10], and the Autoregressive Integrated Moving Average Model (ARIMA) [11] are commonly used in statistical models for load forecasting. In Ref. [8], [PERSON] et al. developed a forecasting model with high accuracy and simple operation based on the exponential smoothing method and analyzed the role of exponential smoothing in load forecasting. In Ref. [11], [PERSON] et al. proposed an autoregressive differential moving average model based on long-term dependence and combined it with the cuckoo search algorithm to optimize the parameters of the prediction model. The experimental results show that the ARIMA model has good prediction results in short-term load forecasting. In Ref. [12], [PERSON] et al. considered the external factors and the power system itself in medium- and long-term load forecasting and used a multilevel recursive regression method for medium- and long-term load forecasting to improve the forecasting accuracy by accurately predicting the important functions of time-varying parameters and related factors. Although statistical model-based methods have the characteristics of relatively simple models and fast prediction, these models usually target linear relational models, which require high smoothness of existing data, and non-linear relationships are more difficult to be captured accurately.