# Short-Term Power Load Prediction of VMD-LSTM Based on ISSA Optimization
## 3 Structure of Prediction Model  
Currently, electric power load data, influenced by diverse factors such as meteorological conditions and user behavioral patterns, exhibit prominent nonlinearity and volatility. These characteristics impose stringent demands on the construction of forecasting models. Therefore, through a comprehensive exploration of data features and the implementation of precise data-fitting strategies, the prediction accuracy can be effectively enhanced.  
This paper presents a data-driven hierarchical framework for short-term power load forecasting. The methodology begins with Variational Mode Decomposition (VMD) to decompose the original load series into intrinsic mode functions (IMFs). Permutation  
Figure 4: Multimodal function fitness curves.  
Figure 3: Unimodal function fitness curves.  
entropy (PE) is used to determine the optimal decomposition mode number \(K\), which quantifies the time-series complexity for optimal decomposition. Concurrently, the Maximum Information Coefficient (MIC) is employed to mine the latent characteristics between the meteorological data (e.g., temperature and humidity) and load values, serving to filter more suitable input features for the prediction model. Subsequently, the filtered meteorological features are reconstructed with the decomposed load components (IMFs) and jointly fed into a Long Short-Term Memory (LSTM) network for prediction. To mitigate subjective hyperparameter tuning, the Improved Sparrow Search Algorithm (ISSA) adaptively optimizes three critical LSTM parameters: the learning rate \(l_{r}\) to govern weight updates, hidden layer neuron counts \(L\) to balance the model capacity, and the training batch size \(N\) to stabilize the gradient estimation. Finally, the predicted results from LSTM are summed and reconstructed to obtain the final predicted data. This framework systematically addresses the load data's nonlinearity and volatility by combining VMD's multi-scale decomposition, the ISSA's bio-inspired optimization, and LSTM's sequence modeling.  
In this paper, we first decompose the original load into a set of intrinsic modal components by VMD and use permutation entropy to determine the optimal decomposition model number K. Then, we input each component and the reconstruction of meteorological factors into the LSTM network as input features for prediction, and, at the same time, we optimize the hyperparameters of the LSTM model by using the ISSA, so as to reduce the effects of subjective factors and a priori knowledge and, thus, better capture the sequence. At the same time, the ISSA is used to optimize the hyperparameters of the LSTM model to reduce the influence of subjective factors and a priori knowledge, so as to better capture the sequence pattern and improve the prediction accuracy; finally, the results are reconstructed to obtain the final prediction results. The overall hierarchical framework of the ISSA-optimized VMD-LSTM short-term power load forecasting is shown in Figure 5.  
Figure 5: Overall frame diagram. In Step 3, each color represents a different modal graph obtained from the decomposition. In Step 6, the red curve represents the Singular Spectrum Analysis (SSA)  
algorithm, the blue curve represents the Improved Singular Spectrum Analysis (ISSA) algorithm, the green curve represents the Sine Cosine Optimization Algorithm (SOA), the yellow curve represents the Whale Optimization Algorithm (WOA), the purple curve represents the Grasshopper Optimization Algorithm (GOA), and the pink curve represents the Particle Swarm Optimization (PSO) algorithm.If you have any further questions, please notify me in a timely manner.