# The Wind and Photovoltaic Power Forecasting Method Based on Digital Twins
## 3 Simulation Experiments
### Analysis of Simulation Experiment Results  
#### 3.4.1 Experimental Analysis of Power Load Forecasting Model  
In this section, a comparative analysis was conducted to compare the performance of the existing mainstream power load forecasting models, including LSTM, CNN, Attention, and Transformer models, with the proposed model WPNet in terms of MSE, MAE, and RMSE values. The input for the models was the weather conditions and power load values at the previous time step, and the output was the predicted power load value for the next time step. By comparing the MSE, MAE, and RMSE values with those of LSTM, CNN, Transformer, and Attention models, the following analysis results were obtained:  
(1) As shown in Figure 7a, the proposed model WPNet in this paper outperforms other models in the Flanders_PV test dataset. As shown in Table 3, the model achieves MSE, MAE, and RMSE values of 1.399 \(\times\) 10\({}^{-3}\), 2.5164 \(\times\) 10\({}^{-2}\), and 3.7409 \(\times\) 10\({}^{-2}\), respectively. (2) As shown in Figure 7b, the proposed model WPNet in this paper outperforms other models in the Wallonia_PV test dataset. As shown in Table 4, the model achieves MSE, MAE, and RMSE values of 1.833 \(\times\) 10\({}^{-3}\), 2.7854 \(\times\) 10\({}^{-2}\), and 4.2808 \(\times\) 10\({}^{-2}\), respectively. (3) As shown in Figure 7c, the proposed model WPNet in this paper outperforms other models in the Flanders_wind test dataset. As shown in Table 5, the model achieves MSE, MAE, and RMSE values of 0.704 \(\times\) 10\({}^{-3}\), 1.8529 \(\times\) 10\({}^{-2}\), and 2.6541 \(\times\) 10\({}^{-2}\), respectively. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{3}{c}{**Flanders\_PV**} \\ \cline{2-4}  & **MSE** & **MAE** & **RMSE** \\ \hline LSTM & 0.001687 & 0.027910 & 0.041070 \\ CNN & 0.001888 & 0.028735 & 0.043455 \\ Transformer & 0.021449 & 0.094731 & 0.146457 \\ Attention & 0.001951 & 0.028327 & 0.044174 \\ WPNet & 0.001399 & 0.025164 & 0.037409 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Flanders_PV accuracy comparison. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{3}{c}{**Wallonia\_Wind**} \\ \cline{2-4}  & **MSE** & **MAE** & **MSE** \\ \hline LSTM & 0.002878 & 0.034731 & 0.053646 \\ CNN & 0.003148 & 0.036817 & 0.056104 \\ Transformer & 0.062681 & 0.171094 & 0.250362 \\ Attention & 0.002993 & 0.035948 & 0.054709 \\ WPNet & 0.002708 & 0.033501 & 0.052042 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Wallonia\_wind accuracy comparison. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{3}{c}{**Wallonia\_PV**} \\ \cline{2-4}  & **MSE** & **MAE** & **MSE** \\ \hline LSTM & 0.002091 & 0.030860 & 0.045723 \\ CNN & 0.002110 & 0.030323 & 0.045937 \\ Transformer & 0.021149 & 0.095309 & 0.145428 \\ Attention & 0.002336 & 0.031742 & 0.048332 \\ WPNet & 0.001833 & 0.027854 & 0.042808 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Wallonia_PV accuracy comparison. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{3}{c}{**Wallonia\_PV**} \\ \cline{2-4}  & **MSE** & **MAE** & **MSE** \\ \hline LSTM & 0.002091 & 0.030860 & 0.045723 \\ CNN & 0.002110 & 0.030323 & 0.045937 \\ Transformer & 0.021149 & 0.095309 & 0.145428 \\ Attention & 0.002336 & 0.031742 & 0.048332 \\ WPNet & 0.001833 & 0.027854 & 0.042808 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Flanders_wind accuracy comparison. Figure 7: Comparison of accuracy across different datasets. **(a)** Error display in the Flanders_PV dataset. **(b)** Error display in the Wallonia_PV dataset. **(c)** Error display in the Flanders_Wind dataset. **(d)** Error display in the Wallonia_Wind dataset. To further demonstrate the effectiveness of the proposed model in predicting wind and PV power loads, the predicted values for the last 500 data points in the test set were visualized. Figure 8 presents a detailed comparison of each model's performance across the four datasets.