## 4 Results
### Comparison and Analysis of the Results
Compared to the Bi-LSTM model, the proposed model reduced RMSE by 13.02%, 22.86%, and 24.76% in sunny, cloudy, and rainy weather, respectively; MAPE was reduced by 20.86%, 33.62%, and 34.28%, respectively, and MAE was reduced by 16.03%, 19.37%, and 12.14%, respectively. Compared to the Conv-LSTM model, the proposed model reduced RMSE by 10.84%, 14.28%, and 16.11% in sunny, cloudy, and rainy weather, respectively; MAPE was reduced by 8.76%, 7.23%, and 15.98%, respectively, and MAE was reduced by 13.07%, 15.26%, and 6.06%, respectively. The comparison results indicate that the model proposed in this paper effectively combined the advantages of both CNN and LSTM methods, and used the attention mechanism to compensate for the deficiency of the LSTM model in retaining key information when the input sequence was long, thereby effectively improving prediction accuracy. The processing time is crucial for real-time applications, where faster predictions are often desirable. In our experiments, the Conv-LSTM-ATT model showed a slightly higher processing time compared to the other models. This increment in time can be attributed to the complexity of the model, especially due to the integration of the attention mechanism. While it does add to the prediction time, the improvement in prediction accuracy (as shown by the lower MAPE, MAE, and RMSE values) could justify this trade-off in contexts where prediction accuracy is more critical than the speed of computation. In this study, Bayesian optimization was applied to adjust the data fusion ratios in a photovoltaic power prediction model. By setting 100 iterations, using the expected improvement (EI) acquisition function to balance exploration and exploitation, and setting the data fusion ratio parameter space from 0% to 100%, the research team comprehensively covered all configurations from no fusion to full fusion. The optimization results revealed the optimal data fusion ratios under different weather conditions as follows: under sunny conditions, 38.72%, 2.36%, 26.83%, and 14.50%; under cloudy conditions, 49.11%, 6.77%, 23.46%, 9.88%, and 17.68%; under snowy/rainy conditions, 30.18%, 12.05%, and 19.45%. These optimized fusion ratios were then applied to the training set data under corresponding weather conditions, followed by evaluation using the Conv-LSTM-ATT prediction model. The experimental design involved comparing the impact of five different data fusion strategies on prediction performance, including the following: \"No Fusion\", using only historical data from the target site; \"Uniform Fusion\", evenly fusing data from all surrounding stations; \"Similarity-Filtered Fusion\", evenly fusing data from nearby stations selected based on similarity; \"Bayesian-Optimized Similarity Fusion\", determining the optimal fusion ratios for nearby stations based on similarity through Bayesian optimization; and \"Actual Values\" as a reference for model prediction accuracy. The experimental results showed that, compared to no fusion and uniform fusion strategies, the similarity-filtered fusion and Bayesian-optimized similarity fusion strategies significantly improved prediction accuracy, particularly the Bayesian-optimized similarity fusion, which performed better than other strategies under all test conditions. These findings indicate that appropriate data fusion strategies can significantly enhance the performance of photovoltaic power prediction models, and Bayesian optimization serves as a powerful tool to effectively implement these strategies, especially in environ \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Indexes** & **LSTM** & **Bi-LSTM** & **Conv-LSTM** & **Conv-LSTM-ATT** \\ \hline R\({}^{2}\) & 0.892 & 0.901 & 0.918 & 0.934 \\ RMSE/W & 0.3226 & 0.3218 & 0.2886 & 0.2421 \\ MAE/W & 0.2487 & 0.2363 & 0.2210 & 0.2076 \\ MAPE/\% & 11.55 & 10.24 & 8.01 & 6.73 \\ Time/s & 556 & 628 & 699 & 785 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Forecast errors of different forecasting models for rainy weather. ments requiring high data diversity and complexity.