# Short-Term Power Load Prediction of VMD-LSTM Based on ISSA Optimization
## 1 Introduction
The literature [22] used EEMD to decompose the power load data into multiple Intrinsic Mode Functions (IMFs) with distinct frequency characteristics. These IMFs were classified into high-frequency and low-frequency components through a systematic zero-crossing rate analysis. Specifically, high-frequency components were modeled using a GRU neural network, while low-frequency components were forecasted via MLR. The hierarchical reconstruction of component-specific predictions was thereafter implemented to generate the final load forecasting results. The final load forecasting results show that most prediction points fall close to the true values without significant single-point errors, with an \(E_{MAPE}\) of 4.86%. This indicates the method's effectiveness in enhancing the prediction stability through the frequency-domain decomposition and adaptive modeling of different components. However, while this model mitigates mode mixing in conventional EMD through controlled noise injection, it may also lead to residual noise. The literature [23] proposes the use of CEEMD to decompose the non-stationary wind power time series to reduce the volatility of the data, then input the components into the KELM model for prediction, and optimize the initial values and thresholds of the KELM prediction model using the WOA. Finally,the predicted values of each component are superimposed to obtain the final wind power prediction results. The literature shows that from EMD to EEMD and then to CEEMD, the values of \(E_{MAE}\), \(E_{RMSE}\), and \(E_{MAPE}\) have all decreased, demonstrating the effectiveness of the CEEMD improvements. Although the CEEMD model solves the noise residual problem existing in EEMD by introducing complementary noise, it is computationally complex and its decomposition effect is poor for power loads that undergo sudden local changes. The literature [24] uses VMD to decompose the load sequence into a number of sub-sequences, then uses TCNs for training at different scales, and finally fuses them through a fully connected layer. Compared with methods like EMD, the \(E_{RMSE}\) decreases significantly when using VMD for data decomposition. Although the introduction of VMD to decompose the sequence avoids the phenomenon of model aliasing, reduces the non-stationarity of the data, and has better noise immunity, VMD is more sensitive to the core parameters, which need to be set manually, such as the number of decomposition modes, K. When the value of K is too large it will result in over-decomposition and produce false components, and when the value of K is too small this will result in under-decomposition and VMD will not be able to extract the features efficiently, and the hyperparameters of the TCN need to be set according to human experience, which has a strong subjectivity. The literature [25] proposes using sample entropy to fully exploit the characteristics of each component decomposed by VMD and measure uncertainty and complexity, thereby reducing the nonlinearity, intermittency, and non-stationarity of the wind speed and improving the prediction accuracy. The prediction achieves an \(E_{MAPE}\) of 2.3397%. Although the introduction of sample entropy has a certain effect on improving the wind speed prediction accuracy, the literature [26] points out that permutation entropy can better describe the frequency changes in signals compared with sample entropy, making it more suitable for time-series prediction. A review of the related methods' forecasting work is presented in Table 1.  
Based on the above literature review, the following gaps in power load forecasting remain. The existing methods directly use decomposition methods, such as VMD and EMD, to decompose the original data without considering the correlation between the data, and it is difficult to find appropriate decomposition parameters.