## 4 Results
### Comparison and Analysis of the Results  
To validate the effectiveness of the Conv-LSTM-ATT model, this study selected one day each of sunny, cloudy, and rainy weather from the three types of weather clustered as the test set for prediction. At the same time, we introduced three deep learning models (LSTM, Bi-LSTM, and Conv-LSTM) as benchmarks for comparison. The model proposed in this study and several baseline models were compared in experiments under the same dataset, that is, only the historical data of the target site were used, and then their prediction results were analyzed and compared. Figures 9-11 show the prediction results of four different models under different weather conditions, and Tables 9-11 show the prediction errors of the centralized models under different weather conditions. In comparing the data from the three tables, it is evident that the model proposed in this article achieved the lowest RMSE values for sunny, cloudy, and rainy weather conditions, which were 0.1636, 0.2358, and 0.2421, respectively, when compared to other models. Table 9 shows that in sunny conditions, the photovoltaic output power fluctuated slightly, and the power curve changed relatively smoothly. Several models could predict the trend of photovoltaic output power. The evaluation indicators R\({}^{2}\) of the LSTM, Bi-LSTM, and Conv-LSTM prediction models were 0.933, 0.942, and 0.951, respectively, and the evaluation indicator R\({}^{2}\) of the model proposed in this article was 0.973, which was higher than the other models, and the effect was the best. Table 10 shows that in cloudy conditions, the continuous movement of clouds caused the solar radiation intensity received by the photovoltaic components to change continuously, leading to large fluctuations in the fitting curve of the predicted and actual values of photovoltaic output power. Table 11 shows that in rainy and snowy weather, the RMSEs of the LSTM, Bi-LSTM, and Conv-LSTM prediction models were 0.3226, 0.3218, and 0.2886, respectively, and the RMSE of the model proposed in this article was 0.2421, which was lower than the other three prediction models. The above analysis indicates that the model proposed in this article has more outstanding prediction effects under three types of weather conditions. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Neighboring PV Stations (K)** & **0** & **1** & **2** & **3** & **4** & **5** & **6** \\ \hline RMSE/W & 0.2341 & 0.2229 & 0.2114 & 0.2083 & 0.2011 & 0.1996 & 0.2157 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Impact of the number of neighboring PV stations on prediction accuracy for cloudy weather. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Neighboring PV Stations (K)** & **0** & **1** & **2** & **3** & **4** & **5** & **6** \\ \hline RMSE/W & 0.2467 & 0.2400 & 0.2349 & 0.2269 & 0.2292 & 0.2307 & 0.2425 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Impact of the number of neighboring PV stations on prediction accuracy for rainy weather. Figure 9: Forecast result under sunny weather. Figure 11: Forecast result under rainy weather. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Indexes** & **LSTM** & **BiLSTM** & **Conv-LSTM** & **Conv-LSTM-ATT** \\ \hline R\({}^{2}\) & 0.919 & 0.924 & 0.949 & 0.965 \\ RMSE/W & 0.3275 & 0.3057 & 0.2751 & 0.2358 \\ MAE/W & 0.2535 & 0.2292 & 0.2181 & 0.1848 \\ MAPE/\% & 8.44 & 6.96 & 4.98 & 4.62 \\ Time/s & 559 & 635 & 689 & 739 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Forecast errors of different forecasting models for cloudy weather. Figure 10: Forecast result under cloudy weather. Compared to the LSTM model, the model proposed in this paper reduced RMSE by 18.28%, 27.99%, and 24.95% in sunny, cloudy, and rainy weather, respectively; MAPE was reduced by 36.76%, 45.26%, and 41.73%, respectively, and MAE was reduced by 24.97%, 27.10%, and 16.53%, respectively.