# Short-Term Power Load Forecasting Using an Improved Model Integrating GCN and Transformer
## 4 Analysis of Experimental Results
### Comparative Experiment
#### 4.5.1 ETTh1 Dataset Experiment  
Table 3 shows the performance comparison of RMSE, MAE, and R\({}^{2}\) between MSGNet-MLLA-Transformer and mainstream models under different prediction step sizes (1, 3, 6, and 12 steps) in the ETTh1 dataset. The experimental data show that the model in this paper has systematic advantages in all prediction scenarios. Its RMSE is, on average, approximately 0.04 lower than the optimal benchmark, and its R\({}^{2}\) is, on average, 1.0% higher. The ranking of model performance is as follows: Ours > MrCAN > MSGNet > GRU/TCN (GRU has the weakest performance) > ARIMA, and the visualization results in Figures 6-9 further verify this ranking. In the visualization of the results, we present the models other than ARIMA, which have relatively small differences. This advantage stems from the fact that MSGNet-MLLA-Transformer can collaboratively achieve multi-scale parsing and long-term dependency capture of spatio-temporal features. In contrast, although MrCAN is good at spatio-temporal joint learning and sample relationship modeling, the insufficiency of multi-scale decomposition leads to error accumulation. The MSGNet prototype has advantages in spatial multi-scale feature extraction but is limited by the bottlenecks of sample relationship modeling and long-term dependency capture. However, traditional models such as GRU and TCN have obvious disadvantages in scenarios with strong spatio-temporal coupling due to the lack of effective spatial feature extraction and cross-sample relationship modeling capabilities (the performance is improved after adding the attention mechanism). These findings not only verify the necessity of multi-dimensional feature joint modeling for power prediction but also highlight the importance of collaborative optimization in dimensions such as spatio-temporal feature decoupling, long-term and short-term dependency balance, and sample relationship mining. Figure 5: Visualization of the target sequences of the three datasets, listed as (**a**) ETTh1; (**b**) ETTm1; and (**c**) Australian electricity load. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{3}{*}{**Metrics**} & \multirow{3}{*}{**Method**} & \multicolumn{5}{c}{**Predicted Length**} \\ \cline{3-6}  & & **1** & **3** & **6** & **12** \\ \hline \multirow{6}{*}{RMSE} & ARIMA & 1.900 & 2.418 & 2.732 & 3.348 \\  & GRU & 0.942 & 1.107 & 1.266 & 1.702 \\  & TCN & 0.806 & 1.206 & 1.511 & 1.968 \\  & GRU-Attention & 0.852 & 0.915 & 1.239 & 1.671 \\  & TCN-Attention & 0.730 & 1.015 & 1.313 & 1.736 \\  & MrCAN & 0.705 & 1.002 & 1.283 & 1.688 \\  & MSGNet & 0.741 & 0.931 & 1.210 & 1.662 \\  & Ours & 0.664 & 0.904 & 1.197 & 1.656 \\ \hline \multirow{6}{*}{MAE} & ARIMA & 1.691 & 2.112 & 2.493 & 3.016 \\  & GRU & 0.772 & 0.745 & 0.940 & 1.311 \\  & TCN & 0.601 & 0.946 & 1.143 & 1.542 \\  & GRU-Attention & 0.668 & 0.664 & 0.913 & 1.272 \\  & TCN-Attention & 0.517 & 0.758 & 1.031 & 1.383 \\  & MrCAN & 0.510 & 0.746 & 0.974 & 1.312 \\  & MSGNet & 0.559 & 0.677 & 0.887 & 1.263 \\  & Ours & 0.481 & 0.642 & 0.863 & 1.262 \\ \hline \multirow{6}{*}{R\({}^{2}\)} & ARIMA & 0.731 & 0.683 & 0.554 & 0.328 \\  & GRU & 0.925 & 0.913 & 0.865 & 0.756 \\  & TCN & 0.945 & 0.877 & 0.807 & 0.675 \\  & GRU-Attention & 0.939 & 0.929 & 0.871 & 0.767 \\  & TCN-Attention & 0.951 & 0.913 & 0.855 & 0.746 \\  & MrCAN & 0.958 & 0.915 & 0.861 & 0.760 \\  & MSGNet & 0.953 & 0.927 & 0.876 & 0.768 \\  & Ours & 0.963 & 0.931 & 0.880 & 0.769 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: RMSE, MAE, and \(\mathbf{R}^{2}\) evaluation metrics of the ETTh1 dataset. Figure 6: _Cont._Figure 6: Comparison chart fitting the \(1\)-step prediction experimental model of the ETTh1 dataset, listed as **(a)** GRU; **(b)** TCN; **(c)** GRU-Attention; **(d)** TCN-Attention; **(e)** MrCAN; **(f)** MSGNet; and **(g)** Ours.