## 5 Conclusions  
The study proposes a wind and solar power prediction method based on the selection of sensitive meteorological features and TCN-ATT-LSTM transfer learning. By using evidence-based optimization theory, the model effectively refines the selection discrepancies from various evaluation metrics. Through knowledge transfer, source domain power generation features are adapted to target domain tasks, enabling accurate wind and solar power forecasting. Validated using data from the wind and solar power station planning in northwestern China, the main conclusions are as follows:  
1. The model improves transfer learning efficiency by performing NWP feature selection prior to transfer, selecting meteorological features with a cumulative contribution of over 85% as input, enhancing the efficiency compared to single evaluation methods;  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Evaluating Indicator** & **WT** & **PV** \\ \hline \multirow{3}{*}{RNN} & MAE & 0.2153 & 0.1314 \\  & RMSE & 0.2524 & 0.1506 \\  & R\({}^{2}\) & \(-\)0.1455 & \(-\)0.0611 \\ \hline \multirow{3}{*}{LSTM} & MAE & 0.2054 & 0.1289 \\  & RMSE & 0.2323 & 0.1509 \\  & R\({}^{2}\) & \(-\)0.0294 & \(-\)0.2460 \\ \hline \multirow{3}{*}{Pre-Trained TCN-ATT-LSTM} & MAE & 0.1029 & 0.032 \\  & RMSE & 0.1468 & 0.0604 \\  & R\({}^{2}\) & 0.7907 & 0.9308 \\ \hline \multirow{3}{*}{Transfer Learning LSTM} & MAE & 0.1207 & 0.0282 \\  & RMSE & 0.1625 & 0.0533 \\ \cline{1-1}  & R\({}^{2}\) & 0.7637 & 0.9524 \\ \hline \multirow{3}{*}{Transfer Learning TCN-ATT-LSTM} & MAE & 0.0811 & 0.0184 \\ \cline{1-1}  & RMSE & 0.1114 & 0.0379 \\ \cline{1-1}  & R\({}^{2}\) & 0.8895 & 0.9771 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of prediction performance of each method.  
The TCN-ATT-LSTM model accurately predicts wind and solar power output in both overall trends and most specific time points, effectively addressing the issue of limited historical data for power forecasting. Compared to traditional deep learning models without transfer learning, pre-trained TCN-ATT-LSTM, and transfer learning with LSTM, this method demonstrates superior performance with significantly higher accuracy in real-world cases;
* As new wind and solar power stations collect more operational data, the target domain dataset for transfer learning will expand, enhancing forecasting accuracy. With improved data completeness, the proportion of target domain samples in the training set will increase, gradually transitioning the approach to deep learning methods.  
Conceptualization, Y.W. and J.Y.; methodology, Y.B.; software, Y.G. and X.L.; validation, Y.Y. and W.S.; formal analysis, Y.B.; data curation, Y.G. and W.S.; writing--original draft preparation, J.Y.; writing--review and editing, Y.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Inner Mongolia Electric Power (Group) Co., Ltd., with its flagship project, 2024-4-67.  
Not applicable.  
Due to the confidentiality requirements of the State Grid company, the research data cannot be disclosed.  
The author [PERSON] is employed by the company Inner Mongolia Electric Power (Group) Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.