# Short-Term Power Load Forecasting Based on an EPT-VMD-TCN-TPA Model
## 4 Experiments
### Ablation Experiments
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **MAPE (\%)** & **RMSE (MW)** & **MAE (MW)** & **R\({}^{2}\)** & **Time (s)** \\ \hline TCN & 2.84 & 247.9006 & 181.3514 & 0.9774 & 34.040 \\ VMD-TCN & 2.59 & 187.9851 & 147.2761 & 0.9870 & 390.934 \\ VMD-TCN-Attention & 1.58 & 133.4030 & 102.7410 & 0.9934 & 383.140 \\ VMD-TCN-TPA & 1.54 & 122.1089 & 98.6245 & 0.9945 & 427.891 \\
**EPT-VMD-TCN-TPA** & **1.25** & **110.2692** & **82.3180** & **0.9955** & **443.232** \\ EPT-VMD-FE-TCN-TPA & 1.86 & 168.9959 & 121.6994 & 0.9895 & 296.137 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Area 1 ablation model evaluation indicators. Figure 17: Area 1 ablation experiment prediction results. As shown in Table 8, the EPT-VMD-FE-TCN-TPA recombination model, which uses the sample entropy (FE) to evaluate the complexity of the IMF, has better prediction results compared to TCN and VMD-TCN. The EPT-VMD-FE-TCN-TPA recombination model has lower prediction precision compared to the EPT-VMD-TCN-TPA model, although operational efficiency is improved due to the reduced computational size. In the next step, we will consider how to use FE more rationally to evaluate the complexity of IMF so that the EPT-VMD-FE-TCN-TPA reorganization model can be run more efficiently with guaranteed accuracy. The autocorrelation function (ACF) and partial autocorrelation function (PACF) of the final residuals of all models in Table 8 are shown in Figure 18. In Figure 18, it can be seen that the final residuals in the EPT-VMD-TCN-TPA short-term load prediction model and the EPT-VMD-FE-TCN-TPA recombination model proposed in this paper have been whitened, which indicates the validity of these two models proposed in this paper. In order to verify the performance of the models proposed in this paper with the ablation comparison models in Table 8, we performed DM tests for each pair of models. The results of the DM tests are listed in Table 9.  
In Table 9, the DM test uses MSE as a metric. The original hypothesis H in the DM test is that the column model and the row model have the same effect. The alternative hypothesis H0 in the DM test is a column model and a row model with different effects. The experimental results are \(p\)-values outside the parentheses and DM statistics inside the parentheses. If the \(p\)-value \(>\) 0.05, the effect of the row model is the same as that of the column model in the comparison; if \(p\)\(<\) 0.05 and the DM value is positive, it means that the effect of the row model is better than that of the column model with which it is compared; and if \(p\)\(<\) 0.05 and the DM value is negative, it means that the effect of the column model is better than that of the row model with which it is compared. As can be seen in the table, the experimental effect of our proposed EPT-VMD-TCN-TPA short-term load prediction model is better than other ablation comparison experiments. The experimental results of the recombinant EPT-VMD-FE-TCN-TPA short-term load prediction model proposed in this paper are statistically inferior to the EPT-VMD-TCN-TPA model proposed in this paper. However, when combined with the results of the classical experimental comparison models in DM inspection, the recombinant model can still achieve good prediction results. Figure 18: _Cont._  
Figure 18: ACF and PACF plots of TCN (**a,b**), VMD-TCN (**c,d**), VMD-TCN-Attention (**e,f**), VMD-TCN-TPA (**g,h**), EPT-VMD-TCN-TPA (**i,j**), EPT-VMD-FE-TCN-TPA (**k,l**).