# Two-Stage Short-Term Power Load Forecasting Based on SSA-VMD and Feature Selection
## 1 Introduction
Secondly, SARIMA is used to forecast the regular subsequence and DBN is used to forecast the random subsequence, and finally, the prediction values of each sub-sequence are summed to result in the final prediction value. In reference [34], a hybrid prediction model based on VMD, BOA and LSTM was constructed and achieved excellent forecasting results. In view of the above advantages of VMD, VMD was chosen to be used to decompose the original load data in this paper. In hybrid forecasting models, the artificial setting of some parameters requires a lot of experience, and makes the parameter setting task difficult, so using a swarm intelligence optimization algorithm to find the optimal parameters for the forecasting model can improve the performance of the forecasting model [35]. Reference [21] used the Grey Wolf Optimization algorithm (GWO) to obtain optimal parameter sets for CNN and BiLSTM. In reference [29], quantum dragonfly algorithm (QDA) was used in combination with SVR. In reference [36], the optimal parameters for SVR are determined using the particle swarm optimization algorithm (PSO). In reference [37], a paper manufacturer was used as the research object to collect tertiary electricity consumption data and establish a hybrid prediction model based on production information back propagation neural network (BPNN) in combination with genetic algorithm (GA) and PSO, in order to obtain more accurate forecasting values and reduce the unit power consumption of paper products. The setting of VMD parameters directly affects the subsequent forecasting effect, so this paper uses SSA to seek the optimal key parameters of VMD. In addition, the light weight of the model is also an issue that should be considered in short-term load forecasting. A forecasting model using GRU and Random Forest (RF) is proposed in reference [38], where GRU is used to predict the electric load, while RF is used to decrease the input dimensionality of the model. The prediction model is constructed with guaranteed accuracy to achieve a lightweight model. Due to the complexity of the various factors that influence the load, directly entering all of them into the prediction model would unnecessarily increase the dimensionality of the feature vector, and would not be conducive to the accuracy of the model. Reference [39] proposes a conditional mutual information-based feature selection method to select a more effective set of input variables for the prediction model. In order to filter out most of the unrelated and redundant features, reference [40] uses the Partial Mutual Information-based filtering method. In reference [41], the Spearman rank correlation coefficient is used to quantitatively analyze the correlation between the building heating load and various variables. By feature selection engineering, the constructed forecasting model is simplified to ensure high forecasting accuracy and forecasting rate. To achieve this goal, this paper takes full consideration of the dimensionality of the feature vector when constructing the feature vector to ensure the light weight of the model. Pearson correlation analysis (PCC) and maximal information coefficient (MIC) are used to select the factors with a high degree of influence on short-term load as feature values and construct the feature vector; the autocorrelation function (ACF) is used to select the node load values that have the greatest influence on the load values of the nodes to be predicted. The various forecasting methods mentioned above are summarized in Table 1 below. This paper proposes a two-stage hybrid forecasting model to fully mine the information in non-linear load data, and to integrate the influencing factors into the forecasting model to improve the accuracy of short-term load forecasting.