# Short-Term Power Load Prediction of VMD-LSTM Based on ISSA Optimization
## 1 Introduction
At the same time, most scholars directly use hybrid models for prediction, and although the prediction accuracy is high, the prediction time is long, and most of the hyperparameters, such as model learning rate and training batch times, rely on experience selection. Therefore, this paper proposes the VMD-LSTM method based on ISSA optimization for power load forecasting. The main contributions of this paper are as follows: \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Reference** & **Model** & **Limitations** & **Optimization** \\ \hline [PERSON], [PERSON] et al. [15] & Wavelet Transform-LSTM & Manually adjust the model parameters & No \\ [PERSON], [PERSON] et al. [21] & EMD-BiLSTM & High computational cost, modal aliasing & No \\ [PERSON], [PERSON] et al. [22] & EEMD-GRU-MLR & Residual noise & No \\ [PERSON], [PERSON], [PERSON] et al. [23] & CEEMD-WOA-KELM & Prone to local optima, sensitive to initial parameters & Yes \\ [PERSON], [PERSON] et al. [24] & VMD-TCN-Multi-Scale & De preprocessing, high implementation requirements & No \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Review of related methods’ forecasting work. 1. This study proposes a novel approach to determine the correlation between meteorological factors and the power load based on the Maximum Information Coefficient (MIC) combined with Variational Mode Decomposition (VMD) for decomposing power load series. Specifically, permutation entropy (PE) is employed to identify the optimal decomposition scale of VMD. PE is a method that can better adapt to the characteristics of signals and improve the accuracy and efficiency of signal decomposition, which can effectively mitigate the volatility and complexity of load data. 2. The Hilbert transform is applied to analytically transform the decomposed input signals, which is followed by constructing a variational problem. By introducing quadratic penalty terms and the Lagrange multiplier method, the augmented Lagrangian function is formulated to transform the constrained variational problem into an unconstrained one, which is then solved via iterative sequence updates. 3. To mitigate the subjective bias and prior knowledge dependency in LSTM networks, an Improved Sparrow Search Algorithm (ISSA) is developed to optimize four key hyperparameters, the learning rate (\(l_{r}\)), number of hidden layer neurons (\(L_{1}\), \(L_{2}\)), and training batch size (\(N\)), thereby enhancing short-term power load forecasting accuracy. 4. Experiments are conducted using datasets with varying input horizons and seasonal patterns from Detu'an City and a region in Australia, aiming to validate the proposed model's stability and flexibility in short-term power load forecasting. The rest of this article is organized as follows. Section 2 firstly introduces the modules of the proposed prediction model, including the MIC, VMD, PE, ISSA, and LSTM. Section 3 describes the framework of our proposed method. The evaluation metrics are shown in Section 4. In Section 5, the case studies on real-world load data from different datasets are used to test the method proposed in this article. Finally, Section 6 concludes this article.