# Two-Stage Short-Term Power Load Forecasting Based on SSA-VMD and Feature Selection
## 1 Introduction
The main contributions of this paper are as follows:  
(1) This paper analyses the power load characteristics and influencing factors, and summarizes the function expression of the current load with which to construct a two-stage hybrid forecasting model, which improves the interpretability of the model; \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Types of** & **References** & **Advantages** & **Improvement Requirement/Disadvantage** \\ \hline Traditional forecasting methods/statistics-based forecasting methods & [4; 5; 6; 7; 8] & This type of method has the advantage of being simple and fast, with a good fit and forecasting for smooth curves. & This type of method is less effective in forecasting load data that are more volatile. \\ \hline Artificial intelligence forecasting methods & [9; 10; 11; 12; 13; 14; 15; 16; 17] & Artificial intelligence methods are more widely applied and have a better forecasting effect. & The single model does not sufficiently tap the information in the historical load data. \\ \hline CNN with RNN combination & [18; 19; 20; 21; 22; 23] & CNN can fully exploit the information in historical load data and influencing factors, and input the extracted feature vectors into RNN for forecasting. This type of method has high forecasting accuracy. & This type of method is less interpretable. \\ \hline Decomposing the original load data before forecasting & [3; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34] & This type of method reduces the volatility of the original load data, and the different components obtained from the decomposition are forecasted individually, with the various methods forming a complementary advantage. & A better decomposition algorithm should be chosen for the forecasting method to avoid mode mixing and to minimize decomposition residuals as much as possible. \\ \hline Using swarm intelligence optimization algorithms in forecasting models to determine optimal parameters & [21; 29; 35; 36; 37] & The method is able to determine the optimal parameters in the model more efficiently and improve the forecasting performance of the model. & Improving the global exploration capability and convergence speed of swarm intelligence optimization algorithms. \\ \hline Using feature selection in forecasting models & [39; 40; 41] & The dimensionality of the feature vectors is reduced. Ensuring the efficient input of feature vectors into the model and ensuring a model is lightweight. & The combination of different feature selection and correlation analysis methods can be considered. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comprehensive analysis of relevant references. (2) An evaluation criterion for VMD decomposition applicable to the field of time-series forecasting is proposed, and SSA is used to optimize the parameters of VMD under this criterion, which reduces the randomness of setting VMD parameters by artificial experience, reduces the loss of original load data decomposition, and improves the decomposition effect;  
(3) Using the optimized VMD to decompose the original load data, the high- and low-frequency components from decomposition are fed into different models for forecasting--the low-frequency components are fed into MLR for forecasting and the high-frequency components are fed into LSTM for forecasting. The trend load values over different time spans are obtained, and the first stage of forecasting is completed;  
(4) Different from previous models in which the component load forecasting values were directly summed up, this paper makes error corrections to the trend load values in the second stage. PCC and MIC are used to select the relevant factors with a high degree of influence; ACF is used to select the node load values with a high influence on the load values of the nodes to be predicted. The fusion of the forecasting values in the first stage and the selected feature values by feature engineering is used to construct feature vectors, which are input into the fully connected layer for forecasting and complete the error correction in the second stage.