## 5 Conclusions  
With the growing demand for higher accuracy in STLF in power systems, particularly in high-volatility regions, this study introduces a hybrid forecasting model based on the LSTM-SCN. Abandoning the traditional fully connected layer forecasting method, this  
Figure 14: Forecasting performance in Region 5.  
Figure 12: Forecasting performance in Region 3.  
Figure 13: Forecasting performance in Region 4.  
study innovatively adopts the SCN, known for its universal approximation property, for the forecasting process. This approach not only reduces the time and effort required to set up the network structure by eliminating the need for repeated attempts but also enables the identification of the optimal network structure among various configurations. The LSTM-SCN model utilizes the LSTM to extract data features, which are then used as inputs to the SCN for forecasting. Before commencing the experiments, we validated the Universal Approximation Property of the SCN on two regression datasets. In this study, we selected the Australian Electricity Load dataset for the experiments and used three metrics--the RMSE, MAE, and MAPE--to evaluate the model's performance. The RMSE, MAE, and MAPE values of the LSTM-SCN model on the Australian dataset are 56.970, 43.033, and 0.492%, respectively, outperforming other models. To evaluate the predictive performance of the LSTM-SCN model in high-volatility regions, five high-volatility areas were selected from the test set for analysis. The LSTM-SCN model achieved the best performance metrics across all selected regions. In Region 1, the RMSE, MAE, and MAPE values for the LSTM-SCN model were 5.465, 4.851, and 0.062%, respectively. Compared to the second-best model, the LSTM-SCN model achieved reductions in errors of 37.901, 29.781, and 0.391% for the RMSE, MAE, and MAPE, respectively. Similarly, in Region 2, the LSTM-SCN model obtained RMSE, MAE, and MAPE values of 7.285, 5.095, and 0.062%, respectively, with reductions in errors of 39.923, 31.502, and 0.389% compared to the second-best model. For the remaining three regions, the LSTM-SCN model consistently outperformed all other models across the three evaluation metrics.  
In summary, the proposed LSTM-SCN hybrid model demonstrates strong performance in short-term power load forecasting, particularly in capturing data variation trends in high-volatility regions, thereby improving forecasting accuracy in these challenging areas. Additionally, the LSTM-SCN model offers a novel solution for STLF, with significant practical application value and promising development potential. This model has the potential to drive new breakthroughs in the field of power load forecasting.  
Conceptualization, B.T. and C.Z.; methodology, B.T and Q.B.; software, B.T. and Q.B; validation, B.T., C.Z., and M.Y.; data curation, B.T.; writing--original draft preparation, B.T.; writing--review and editing, B.T. and J.H.; visualization, B.T.; supervision, J.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Guizhou Provincial Science and Technology Fund (Qian Kehe Basic-ZK [2021] General 337), supported by the Fund of the State Key Laboratory of Public Big Data, Guizhou University (No. PBD2023-35), and the Graduate Program of Guizhou University of Finance and Economics (2022 ZSY036).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.