# Predicting the Performance of Solar Power Generation Using Deep Learning Methods
## 4 Experiments and Discussion
### Experimental Results and Discussion  
In this research process of experiment, besides the importance of aggregating strong correlative feature variables with generating solar power, the selection of prediction models is also to improve the performance. The most accurate and correlative feature variables are utilized to compare the effectiveness of different neural network models, which are the SimpleRNN, LSTM, GRU, Bi-SimpleRNN, Bi-LSTM, and Bi-GRU. The experimental program A-11 which has achieved the best performance measurement indicators, is utilized to demonstrate the effectiveness and accuracy during the training process. The originally configured in the initial stage between experimental models were identical, and the differences and changes were discussed. Stage 2 experimental summary of experimental performance using different models with measurement indicators among various programs is shown in Table 6.  
Firstly the neural network models were executed separately several times, which selected the best performance experimental results for discussion. The training process and solar generation prediction in different sceneries are shown in Figures 3-8. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Experiment** & **Data Set with Different Models** & **Score (\(r^{2}\))** \\ \hline B-1 & Data set A-11 with LSTM model & 0.914729 \\ B-2 & Data set A-11 with GRU model & 0.859122 \\ B-3 & Data Set A-11 with SimpleRNN model & 0.733053 \\ B-4 & Data set A-11 with Bidirectional-LSTM model & 0.904538 \\ B-5 & Data set A-11 with Bidirectional-GRU model & 0.934156 \\ B-6 & Data set A-11 with Bidirectional-SimpleRNN model & 0.765555 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Summary of experimental performance among different models with measurement indicators (Stage 2). \begin{table}
\begin{tabular}{c c} \hline \hline
**Program** & **Evaluation Score (\(r^{2}\))** \\ \hline A-1 & 0.849865 \\ A-2 & 0.853013 \\ A-3 & 0.86099 \\ A-4 & 0.865523 \\ A-5 & 0.880833 \\ A-6 & 0.876786 \\ A-7 & 0.872881 \\ A-8 & 0.835199 \\ A-9 & 0.836241 \\ A-10 & 0.845699 \\ A-11 & 0.890179 \\ A-12 & 0.884947 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Pearson correlation coefficient comparison table. \begin{table}
\begin{tabular}{c c} \hline \hline
**Performance Measure** & **Score** \\ \hline mean\_squared\_error & 0.010779 \\ mean\_absolute\_error & 0.076686 \\ mean\_squared\_log\_error & 0.005071 \\ r\({}^{2}\)\_score & 0.890179 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summary of experimental performance measurement indicators (Stage 1). Figure 3: _Cont._  
Figure 4: _Cont._  
Figure 3: Multiple inverters and model predictions with the same A-11 feature variable information (LSTM). Figure 4: Multiple inverters and model predictions with the same A-11 feature variable information (GRU). Figure 5: _Cont._  
Figure 5: Multiple inverters and model predictions with the same A-11 feature variable information (SimpleRNN). Figure 6: Cont. Figure 6: Multiple inverters and model predictions with the same A-11 feature variable information (BiLSTM). Figure 7: Cont. Figure 3:0 _Cont_. Figure 3:1 Multiple inverters and model predictions with the same A-\(11\) feature variable information (BiGRU). From Table 5, the SimpleRNN, which is essentially the most intuitive and most basic regression model, has less performance comparing with the improved LSTM and GRU. In addition, although the GRU is an improved version of LSTM and the training time required  
Figure 3:2 Multiple inverters and model predictions with the same A-11 feature variable information (BiRNN). for GRU is indeed slightly reduced, the performance of GRU predictions generally cannot be equal to or exceed the performance of LSTM. Therefore, by exploring the conclusions of the differences in the effectiveness of the above three algorithms, the SimpleRNN is recommended for basic simple calculations.