## 5 Conclusions  
This study employed the correlation analysis to identify and refine input variables, aiming to reduce their dimensionality and simplify the computational process. A data-driven framework was introduced, which integrated spatial and temporal information. This framework effectively leveraged the advantages of both CNN and LSTM networks by developing the Conv-LSTM module, thereby enhancing the model's ability to learn the long-term mapping relationship between photovoltaic power and meteorological data. By integrating attention mechanisms into the Conv-LSTM model, distinct weights were assigned to LSTM's hidden layers, reducing the loss of historical information and intensifying the impact of crucial data. Under the same dataset conditions, the experimental results of the study indicated that compared to three classical models, the method proposed in this paper exhibited superior performance in terms of prediction. Moreover, the utilization of a fused dataset further amplified the model's performance, showcasing its exceptional predictive capabilities. Nevertheless, the model's scalability and generalization capability across different geographical locations and varying environmental conditions need further investigation. It is important to assess how well the model performs when applied to data from different PV systems that were not part of the initial training dataset. To address this, future research could focus on employing advanced techniques like transfer learning and domain adaptation. This approach would enable the model to effectively adapt to diverse environmental \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Indexes** & **No Fusion** & **Uniform Fusion** & \
\begin{tabular}{c} **Similarity-Filtered** \\ **Fusion** \\ \end{tabular} &
\begin{tabular}{c} **Bayesian-Optimized** \\ **Similarity Fusion** \\ \end{tabular} \\ \hline R\({}^{2}\) & 0.965 & 0.972 & 0.980 & 0.985 \\ RMSE/W & 0.2358 & 0.1939 & 0.1747 & 0.1692 \\ MAE/W & 0.1848 & 0.1702 & 0.1675 & 0.1523 \\ MAPE/\% & 4.62 & 4.05 & 3.91 & 3.75 \\ Time/s & 739 & 783 & 836 & 853 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Comparison of the prediction errors of rainy weather under different fusion ratios. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Indexes** & **No Fusion** & **Uniform Fusion** & \
\begin{tabular}{c} **Similarity-Filtered** \\ **Fusion** \\ \end{tabular} &
\begin{tabular}{c} **Bayesian-Optimized** \\ **Similarity Fusion** \\ \end{tabular} \\ \hline R\({}^{2}\) & 0.973 & 0.983 & 0.991 & 0.996 \\ RMSE/W & 0.1636 & 0.1418 & 0.1345 & 0.1308 \\ MAE/W & 0.1283 & 0.1122 & 0.1063 & 0.0987 \\ MAPE/\% & 4.06 & 3.82 & 3.25 & 2.83 \\ Time/s & 755 & 794 & 824 & 876 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Comparison of the prediction errors of sunny weather under different fusion ratios. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Indexes** & **No Fusion** & **Uniform Fusion** & \
\begin{tabular}{c} **Similarity-Filtered** \\ **Fusion** \\ \end{tabular} &
\begin{tabular}{c} **Bayesian-Optimized** \\ **Similarity Fusion** \\ \end{tabular} \\ \hline R\({}^{2}\) & 0.934 & 0.953 & 0.966 & 0.976 \\ RMSE/W & 0.2421 & 0.2179 & 0.1828 & 0.1672 \\ MAE/W & 0.2076 & 0.1813 & 0.1652 & 0.1425 \\ MAPE/\% & 6.73 & 5.16 & 4.99 & 4.76 \\ Time/s & 785 & 798 & 863 & 898 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Comparison of the prediction errors of cloudy weather under different fusion ratios. variables and different geographic locations, ensuring robust performance across a variety of PV systems. Additionally, enriching the training dataset with a broader spectrum of climatic and geographical data could further enhance the model's predictive accuracy and reliability in new settings. Conceptualization, F.H. and L.Z. ; methodology, F.H. ; software, F.H. and J.W. ; validation, F.H., L.Z. and J.W. ; formal analysis, F.H. ; investigation, J.W. ; resources, L.Z. ; data curation, F.H. ; writing--original draft preparation, F.H. ; writing--review and editing, J.W. ; visualization, F.H.