## 4 Case Study and Analysis
### Analysis of TCN-ATT-LSTM Network Transfer Learning Prediction Results  
After preprocessing the source domain data, the TCN-ATT-LSTM combined neural network is used for pretraining. The pretrained feature network is then fine-tuned using one month of target domain data, resulting in a transfer learning prediction network adapted to the target domain. Figure 6 illustrates the prediction results for wind and solar power using the transfer learning method. To evaluate the accuracy of the model's transfer performance, the differences between the predicted wind and solar power curves and the actual power curves in the target domain are calculated using metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (R-square, R\({}^{2}\)). The relevant calculation formulas are shown in Equations (9)-(11), as follows:  
\[\text{MAE}=\frac{1}{I}\sum\limits_{i=1}^{I}\left|y_{i}-\hat{y}_{i}\right| \tag{9}\]  
\[\text{RMSE}=\sqrt{\frac{1}{I}\sum\limits_{i=1}^{I}(y_{i}-\hat{y}_{i})^{2}} \tag{10}\]  
\[\text{R}^{2}=1-\frac{\sum\limits_{i=1}^{I}(y_{i}-\hat{y}_{i})^{2}}{\sum\limits _{i=1}^{I}(y_{i}-\overline{y})^{2}} \tag{11}\]  
where the number of prediction points is denoted by \(I\), actual values of wind and solar power are represented by \(y_{i}\), and the predicted values of WT and PV power are represented by \(\hat{y}_{i}\).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**Selection Results of Wind Power Meteorological Characteristics**} \\ \hline \multirow{2}{*}{**Meteorological Characteristics**} & **Pearson** & **Mutual** & \multirow{2}{*}{**Spearman**} & **Contribution Rate After** \\  & **Correlation** & **Information** & & **Dual Optimization** \\  & **Index** & **Entropy Value** & & **Combination** \\ \hline
**Wind speed at 100 m height** & **0.80719** & **0.902547** & **0.807** & **0.278669** \\
**Wind speed at 70 m height** & **0.841589** & **0.925733** & **0.8415** & **0.284937** \\
**Wind speed at 30 m height** & **0.830482** & **0.848707** & **0.8199** & **0.247796** \\
**Wind speed at 10 m height** & **0.778506** & **0.667219** & **0.7608** & **0.16816** \\ Wind direction at 100 m height & 0.01 & 0.987157 & 0.01 & 0.0000788 \\ Wind direction at 70 m height & 0.01 & 1.019574 & 0.01 & 0.0000809 \\ Wind direction at 30 m height & 0.01 & 1.023214 & 0.01 & 0.0000816 \\ Wind direction at 10 m height & 0.01 & 1.050781 & 0.01 & 0.0000831 \\ Cloud cover & 0.174265 & 0.811323 & 0.2063 & 0.014064 \\ Radiation flux & 0.095408 & 0.771664 & 0.0917 & 0.005246 \\ Large-scale precipitation & 0.099563 & 0.02651 & 0.2149 & 0.000279 \\ \hline \multicolumn{5}{c}{**Selection Results of PV Meteorological Characteristics**} \\ \hline \multicolumn{5}{c}{**Sunshine intensity**} & **0.828158** & **0.911613** & **0.8525** & **0.225** \\
**PV panel temperature detection** & **0.81939** & **0.89268** & **0.8390** & **0.216** \\
**Global horizontal irradiance** & **0.866445** & **0.913612** & **0.8633** & **0.235** \\
**UV index** & **0.825131** & **0.766496** & **0.8326** & **0.183** \\ Atmospheric temperature & 0.50876 & 0.5556 & 0.5375 & 0.0701 \\ Mean wind speed & 0.352289 & 0.442447 & 0.2515 & 0.038 \\ Maximum wind speed & 0.287183 & 0.952151 & 0.2557 & 0.032 \\ Humidity & 0.161997 & 0.024704 & 0.3528 & 0.0005 \\ Air pressure & 0.058114 & 0.026726 & 0.3339 & 0.0000179 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Result of source domain NWP feature filtering.  
For the wind power test set, the MAE, RMSE, and R\({}^{2}\) errors are 0.0811, 0.1114, and 0.8895, respectively. For the solar power test set, the MAE, RMSE, and R\({}^{2}\) errors are 0.0184, 0.0379, and 0.9771, respectively. The errors mainly occur during steep rises or drops in the power generation curve. The analysis indicates that the transfer learning model performs well in predictions, not only accurately capturing the overall trend but also achieving high prediction accuracy at most precise time points. Overall, the validation results are satisfactory.