# Short-Term Power Load Prediction of VMD-LSTM Based on ISSA Optimization
## 6 Conclusions  
To tackle the challenges of the inadequate adaptability to non-stationarity and poor precision in capturing nonlinear characteristics inherent in traditional short-term power load forecasting methods, this paper proposes a novel forecasting model based on the Improved Sparrow Search Algorithm (ISSA). The key contributions are summarized as follows:  
(1) The model decomposition of load sequences is conducted via Variational Mode Decomposition (VMD). This approach effectively resolves the mode mixing issue commonly found in decompositions like Empirical Mode Decomposition (EMD), decomposing nonlinear and fluctuating data into stable, regular subsequences with clear physical meanings. (2) Permutation entropy is employed to determine the optimal number of decomposition modes, which improves the accuracy and efficiency of the signal decomposition, ensures optimal decomposition, enhances decomposition precision, and optimizes the prediction performance. (3) The Long Short-Term Memory (LSTM) network is utilized to integrate decomposed sequences with meteorological factors for prediction. This approach effectively extracts the temporal characteristics and complex nonlinear relationships of load data, thereby fully excavating data characteristics and improving the prediction accuracy compared to other models. (4) The ISSA is adopted to optimize the hyperparameters of LSTM. Compared with other algorithms, the ISSA optimization algorithm used in this study features a lower complexity and achieves a better optimization performance. Minimizing the influence of subjective factors and prior knowledge, this optimization achieves the best prediction performance of the VMD-LSTM framework. In summary, the method proposed in this paper has a greater precision and better generalization performance for short-term power load forecasting. In future research, we can consider using data from different industries for forecasting, selecting more efficient models and optimization algorithms, or conducting forecasting research for loads affected by emergencies, so as to further improve the forecasting performance of the model. Conceptualization, S.W. and H.C.; methodology, S.W. ; software, S.W. ; validation, S.W. and H.C.; formal analysis, S.W. and H.C.; investigation, S.W. and H.C.; data curation, S.W. ; writing--original draft preparation, S.W. and H.C.; writing--review and editing, S.W. and H.C.; visualization, S.W. ; supervision, H.C.; project administration, H.C.; funding acquisition, S.W. and H.C. All authors have read and agreed to the published version of the manuscript. This research was partially funded by the Innovation Fund for Industry-University-Research in Chinese Universities (2024 HY031). Not applicable. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **RMSE** & **MAE** & **MAPE** & \(\mathbf{R^{2}}\) \\ \hline VMD+GRU & 64.964 & 52.356 & 0.0053 & 0.9977 \\ VMD+LSTM & 110.84 & 88.078 & 0.0089 & 0.9933 \\ VMD+BiLSTM & 97.052 & 76.839 & 0.0078 & 0.9949 \\ VMD+SSA-LSTM & 51.457 & 42.298 & 0.0043 & 0.9986 \\ VMD+ISSA-LSTM & 28.892 & 23.527 & 0.0024 & 0.9995 \\ VMD+ISSA-GRU & 31.62 & 26.01 & 0.0026 & 0.9995 \\ VMD+SOA-LSTM & 32.366 & 26.274 & 0.0026 & 0.9994 \\ VMD+SOA-GRU & 49.082 & 40.303 & 0.0041 & 0.9987 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Prediction error index of each model. Data Availability Statement: Publicly available datasets were analyzed for this study. Example 1 can be found here: [[https://archive.ics.uci.edu/dataset/849/power+consumption+of+etouan+city](https://archive.ics.uci.edu/dataset/849/power+consumption+of+etouan+city)]([https://archive.ics.uci.edu/dataset/849/power+consumption+of+etouan+city](https://archive.ics.uci.edu/dataset/849/power+consumption+of+etouan+city)) (accessed on 11 February 2025). Example 2 can be found here: [[https://gitcode.com/qq_42998340](https://gitcode.com/qq_42998340)]([https://gitcode.com/qq_42998340](https://gitcode.com/qq_42998340)) /Australia.