# Two-Stage Short-Term Power Load Forecasting Based on SSA-VMD and Feature Selection
## 1 Introduction
Additionally, to solve the vanishing gradient problem when predicting long sequences, reference [20] introduced an attention mechanism into RNNs. In addition, short-term load models were constructed in references [21; 22] using a combination of CNN and BiLSTM, and CNN and BiGRU, respectively. In reference [23], a residual convolutional neural network (R-CNN) is used to extract the basic features of power consumption data, which is input into a multilayer long- and short-term memory network (ML-LSTM) to learn the sequence information, and finally, a fully connected layer is used for prediction. These methods are effective in improving the accuracy of short-term load prediction, but the interpretability of the forecasting models constructed based on such ideas is not strong. Another way of thinking about hybrid models is to build a short-term forecasting model by decomposing the original load data in order to reduce their volatility, and the different components obtained from the decomposition are predicted using their own approaches, with the various methods forming complementary strengths. Wavelet decomposition is used in reference [24], but different basis functions and orders have different effects when using wavelet decomposition to deal with unstable sequences, which makes it a priori and increases the complexity of its use. The empirical mode decomposition (EMD) approach is used in references [3; 25], but EMD suffers from mode confounding problems. The references [26; 27; 28] use an ensemble empirical mode decomposition (EEMD) approach to obtain multiple intrinsic mode functions (IMFs), with separate predictions for different IMFs. Based on this, EEMD has been improved to obtain a complete ensemble empirical mode decomposition algorithm (CEEMD), which has been used to decompose the raw load sequence and construct prediction models [29; 30; 31]. An EMD-mRMR-FOA-GRNN model was constructed in reference [3], where EMD was first used to decompose the raw load series into several IMFs and a residual with different frequencies, and then the correlation analysis of each IMF with features such as day type, temperature and meteorological conditions was performed using the minimal redundancy maximal relevance (mRMR). This resulted in the best feature set. Finally, the fruit fly optimization algorithm (FOA) was used to optimize the smoothing factor in the generalized regression neural network (GRNN), and the final forecast load was obtained by summing the results of all IMF forecasting values. The prediction accuracy of this model was significantly improved. In reference [27], the original load data were decomposed and reconstructed with components with similar entropy values, and the reconstructed components were fed into the LSTM for forecasting, while the hyperparameters in the LSTM were optimized using the Bayesian Optimization Algorithm (BOA). Variational mode decomposition (VMD) is also a commonly used decomposition approach for short-term load forecasting [32; 33; 34], which is a completely non-recursive model that avoids the mode-confounding problems present in EMD, and has the advantage that the number of mode decompositions can be artificially determined. In reference [32], VMD decomposes the original sequence into multiple subsequences, which reduces the volatility of the raw load. CNN is used to solve the problem whereby it is difficult for GRU to extract high-dimensional characteristics of the power load, and an attention mechanism is introduced to solve the problem whereby important information cannot be emphatically weighted when the series is too long. A hybrid model based on the cuckoo search algorithm (CSA), optimizing VMD, seasonal autoregressive integrated moving average (SARIMA) and a deep belief network (DBM), is proposed in reference [33]. Firstly, VMD-CSA decomposes the original load into a number of regular and random sub-series.