## 4 Conclusions  
The paper proposes a combined method for short-term load forecasting that considers the characteristics of components of STL. The proposed combined method utilizes STL and applies different prediction methods to different components. Through specific case analysis, the following conclusions are drawn:  
1. Compared to the LSTM, CNN, GPR, STL-LSTM, STL-CNN, and STL-GPR models, the proposed combined method demonstrates higher prediction accuracy, indicating that applying different prediction methods to the characteristics of the STL components can effectively improve prediction accuracy.
2. The proposed combined method comprehensively considers the trend, periodic, and random characteristics of the load sequence. It can effectively extract the trend and periodic characteristics of the load sequence while taking into account the uncertainty of load variations.
3. The proposed combined forecasting method, which integrates STL with deep learning technologies, has shown considerable improvements in predicting accuracy and stability. Although this method offers advantages over traditional forecasting models, there is still room for enhancement when dealing with the complexity and dynamics of the power system. Therefore, future research will concentrate on integrating a broader range of data sources, including meteorological conditions, economic indicators, and electricity market prices, with the aim of enhancing the model's predictive capabilities for power load fluctuations by incorporating both macro and micro factors. Additionally, the exploration of novel data decomposition techniques and the adoption of more sophisticated machine learning algorithms will be a primary focus of future research aiming to reveal the intrinsic characteristics of data, thereby boosting the accuracy and reliability of predictions. Through thorough research and implementation in these key areas, it is anticipated that not only will the overall performance of short-term power load forecasting be significantly enhanced but also more robust and precise decision support will be provided for the effective and reliable operation of the power system. These efforts will further enhance the adaptability and robustness of forecasting models in response to the intricate dynamics of the power system, establishing a solid foundation for addressing the challenges encountered by future energy systems.  
Conceptualization, S.H. and J.Y.; methodology, C.C.; software, Y.Y. and Y.W.; validation, Y.Z. and Y.G.; formal analysis, C.C.; data curation, S.H. and W.C.; writing--original draft preparation, J.Y.; writing--review and editing, S.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Inner Mongolia Electric Power (Group) Co., Ltd., Science and Technology Project 2023-4-6.  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Prediction Method** & **MAPE** & **MAE (MW)** & **RMSE (MW)** \\ \hline The proposed combined method & 0.14\% & 151.3506 & 265.1293 \\ LSTM & 2.52\% & 1421.0175 & 1756.1287 \\ CNN & 1.69\% & 893.1694 & 1014.3561 \\ GPR & 1.74\% & 961.3262 & 1225.6124 \\ STL-LSTM & 0.81\% & 350.3907 & 432.0894 \\ STL-CNN & 0.60\% & 288.1016 & 397.1486 \\ STL-GPR & 1.05\% & 521.2655 & 660.9917 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Values of evaluation indicators for prediction results.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Due to the confidentiality requirements of the State Grid company, the research data cannot be disclosed.  
**Conflicts of Interest:** Authors [PERSON] and [PERSON] were employed by the company Inner Mongolia Electric Power (Group) Co., Ltd. The remaining authors declare that the re-search was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.