# Short-Term Power Load Forecasting Using an Improved Model Integrating GCN and Transformer
## 5 Conclusions  
To address the problem that the existing short-term load forecasting research often ignores the modeling of multi-scale spatio-temporal features, an MSGNet-MLLA-Transformer method for this task scenario is proposed. Specifically, the MSGNET-MLLA-Transformer proposed in this paper integrates Transformer and enhances the attention paid to MSGNet and MLLA. The experimental results of the sliding time window on the ETH1, ETTm1, and Australian power load datasets all show that a smaller time window size cannot improve the prediction performance of the model, while an overly large time window size leads to a decline in the prediction performance of the model. The most suitable time window sizes are 12, 18, and 24, respectively. The results of the comparative experiments and ablation experiments show that the prediction performance of the deep learning model is superior to that of the ARIMA statistical model, and the model with the ability to extract multi-scale spatio-temporal features is superior to the time-dependent extraction model and the spatial extraction model. In the multi-scale spatio-temporal feature extraction model, the model proposed in this paper can collaboratively achieve multi-scale parsing of spatio-temporal features and capture of long-term dependencies, thus achieving the best results. Due to the limitations of our research goals and space, the interpretability module and probability prediction module were not systematically integrated. In the future, we will further carry out research on interpretability, probability prediction, and other aspects to resolve the problems of insufficient post-event interpretability and the insufficient uncertainty prediction ability of the model in relation to practical applications. 1. We plan to introduce SHAP value analysis to quantify the marginal contribution of input features (such as historical load and spatio-temporal nodes) to the prediction results;
2. We aim to visualize cross-scale attention maps and analyze the attention patterns of the model at different time resolutions (such as minute-level fluctuations and hourly cycles);
3. By using probability prediction methods, we aim to obtain the interval probability distribution of the predicted values to enhance the practical application value of the prediction results. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Predicted Length**} \\ \cline{2-4}  & **Training Time** & **Inference Time** & **RMSE** \\ \hline Ours & 0.178 s & 0.0457 s & 783.182 \\ \hline MSGNet & 0.266 s & 0.084 s & 828.189 \\ \hline MrCAN & 0.087 s & 0.025 s & 884.175 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Computational cost of MSGNet-MLLA-Transformer on the Australian electricity load dataset. **Author Contributions:** Methodology, M.W. and C.C. ; software, M.W., W.F. and C.C. ; validation, M.W. and W.F. ; formal analysis, M.W., W.F. and C.C. ; data curation, M.W., W.F. and C.C. ; writing--original draft preparation, M.W., W.F., X.L. and C.C. ; writing--review and editing, M.W., W.F., X.L. and C.C. ; visualization, M.W., X.L. and C.C. ; supervision, M.W. and Y.L. ; project administration, M.W. and Y.L. ; and funding acquisition, M.W. and Y.L. All authors have read and agreed to the published version of the manuscript. **Funding:** This project was supported by the \"Rising Star in the South China Sea\" project of Hainan Province (NHDXRCXM202322) and the Education Department of Hainan Province (Hnky2024-76). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable.