## 3 Features Selection and TCN-ATT-LSTM Transfer Learning  
### Selection Method for Sensitive Meteorological Features  
In this study, Pearson's correlation coefficient, mutual information entropy, and Spearman's rank correlation coefficient are employed as indicators for selecting sensitive meteorological feature factors [19]. The respective formulas for these indices are shown in Equations (1)-(3):  
\[P(S,X)=\frac{\sum\limits_{i=1}^{n}\left(S_{i}-\overline{S}\right)\left(X_{i}- \overline{X}\right)}{\sqrt{\frac{\sum\limits_{i=1}^{n}\left(S_{i}-\overline{S }\right)^{2}}{\sum\limits_{i=1}^{n}\left(X_{i}-\overline{X}\right)^{2}}}} \tag{1}\]  
\[\rho=\frac{\sum\limits_{i=1}^{n}\left(G_{i}-\overline{G}\right)\left(H_{i}- \overline{H}\right)}{\sqrt{\sum\
olimits_{i=1}^{n}\left(H_{i}-\overline{H} \right)^{2}\sum\
olimits_{i=1}^{n}\left(G_{i}-\overline{G}\right)^{2}}} \tag{3}\]  
where \(P(S,X)\) represents the Pearson correlation coefficient. A value closer to 1 indicates a stronger positive correlation between the two datasets, while a value nearer to \(-1\) signifies a stronger negative correlation. \(S\) denotes the meteorological feature sequence, while \(X\) represents the wind and solar power output sequence. \(n\) is the dimension of the corresponding sequence, and \(\overline{S}\) and \(\overline{X}\) are the mean values of the two sequences, respectively.  
\(H\) represents information entropy. Mutual information entropy reflects the degree of association between two variables. The smaller the association, the lower the mutual information entropy. When two variables are completely independent, the mutual information entropy is minimized (equal to zero).  
\(G_{i}\) represents the value level of the \(i\)-th dimension in the meteorological feature sequence, and \(H_{i}\) represents the value level of the \(i\)-th dimension in the wind-solar power sequence. \(\overline{G}\) represents the mean value level of the meteorological feature sequence, and \(\overline{H}\) represents the mean value level of the wind-solar power sequence. The value of \(\rho\) ranges from \(-1\) to \(1\). A higher \(|\rho|\) value indicates a stronger correlation between the parameters.  
The three evaluation metrics mentioned above each possess the ability to measure the relationship between NWP data and power output. However, relying on a single metric cannot fully capture the complexity of this relationship. Therefore, this study employsevidence-based optimization theory to combine these metrics, resulting in a final composite confidence score given by Equation (4), as follows:  
\[m(a,b)=\begin{cases}\frac{\sum\limits_{a\
eq b=A}m_{1}(a)m_{2}(b)}{1-\sum \limits_{a\
eq b=A}m_{1}(a)m_{2}(b)},&A\
eq\varnothing\\ 0,&A=\varnothing\end{cases} \tag{4}\]  
where \(m(\square)\) represents the trust function, which calculates the correlation between NWP data and power output based on the Pearson correlation coefficient, mutual information entropy, and the Spearman correlation coefficient.  
The dual optimization combination is shown in Equation (5), as follows:  
\[m(ab,c)=\begin{cases}\frac{\sum\limits_{a\
eq b=A}m_{1}(ab)m_{2}(c)}{1-\sum \limits_{a\
eq b=A}m_{1}(ab)m_{2}(c)},&A\
eq\varnothing\\ 0,&A=\varnothing\end{cases} \tag{5}\]  
On this basis, the optimized combination confidence scores of the meteorological features are ranked in descending order. Then, NWP features with a cumulative contribution rate exceeding 85% are selected [20], thereby achieving the selection of key meteorological features. And its flowchart is shown in Figure 2.