# Assessment of Different Deep Learning Methods of Power Generation Forecasting for Solar PV System
## 5 Numerical Results
### Forecast Performance with Different Weather Data Groups
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**CWB Weather Data**} & \multicolumn{3}{c}{**Local Weather Data**} & \multicolumn{3}{c}{**Hybrid Weather Data**} & \multicolumn{3}{c}{**Average**} \\ \cline{2-13}  & **MAPE** & **MAE** & **RMSE** & **MAPE** & **MAE** & **RMSE** & **MAPE** & **MAE** & **RMSE** & **MAPE** & **MAE** & **RMSE** \\ \hline ANN & 29\% & 2.020 & 1.400 & 23\% & 1.415 & 1.179 & 24\% & 1.333 & 1.147 & 25.3\% & 1.598 & 1.242 \\ LSTM & 21\% & 0.931 & 0.940 & 23\% & 1.839 & 1.240 & 16\% & 0.706 & 0.831 & 20.0\% & 1.158 & 1.004 \\ GRU & 19\% & 1.083 & 1.003 & 23\% & 2.207 & 1.362 & 20\% & 0.828 & 0.888 & 20.7\% & 1.372 & 1.084 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Average one-day-ahead PV power forecasting results of each weather data group for one week. Figure 9: The PV power forecasting results of the GRU model. big change in weather conditions, the difference between the forecast and actual power generation trends will become more pronounced. When comparing the daily forecast numerical analysis results of the LSTM and GRU models for one week, the results of the LSTM model are better than those of the GRU model, and the average forecast results are relatively stable. The average results of LSTM for a week have a MAPE of 16%, MSE of 0.71, and RMSE of 0.83. On the other hand, the GRU model is better than LSTM on sunny days. The MAPE, MAE, and RMSE of GRU are 8%, 0.263, and 0.513, and those of LSTM are 11%, 0.531, and 0.792, respectively, on 27 June 2021. In addition, the forecast results of GRU on 2 July are also better than those of LSTM. However, the advantages of the LSTM model are highlighted when the weather pattern is poor. From the forecasting results on 1 July, the MAPE of LSTM is about 8% higher than that of GRU, and MAE and RMSE are about 0.3-0.4 higher, respectively. Therefore, when selecting the prediction results for the next day, the weather forecast can be used as a reference. If the weather forecast for the next day is sunny, the results of the GRU model are more accurate. If it is cloudy, the results of LSTM are a better choice. According to the aforementioned results, it is found that forecasting via the LSTM model combined with the hybrid weather data yields the best prediction results. Therefore, the LSTM model was used to predict the solar photovoltaic power for June 2021, and the results are shown in Figure 11. The results show that MAPE, MAE, and RMSE are 16.984%, 1.764, and 1.283, respectively. It is shown that the combination of CWB data and LWS data together with the deep learning model is helpful for improving the accuracy of the one-day-ahead solar photovoltaic forecast. Furthermore, a comparison with other one-day-ahead solar photovoltaic forecast methods from the literature is shown in Table 10. It can be found that the application of complex deep learning algorithms combined with  
Figure 10: Comprehensive comparison results of hybrid metadata. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Day**} & \multicolumn{3}{c}{**ANN Model**} & \multicolumn{3}{c}{**LSTM Model**} & \multicolumn{3}{c}{**GRU Model**} \\ \cline{2-10}  & **MAPE** & **MAE** & **RMSE** & **MAPE** & **MAE** & **RMSE** & **MAPE** & **MAE** & **RMSE** \\ \hline
27 June 2021 & 15\% & 1.090 & 1.044 & 11\% & 0.531 & 0.729 & 8\% & 0.263 & 0.513 \\
28 June 2021 & 35\% & 1.863 & 1.365 & 21\% & 1.046 & 1.023 & 25\% & 1.205 & 1.098 \\
29 June 2021 & 31\% & 1.506 & 1.227 & 23\% & 0.861 & 0.928 & 26\% & 0.864 & 0.930 \\
30 June 2021 & 20\% & 0.968 & 0.984 & 13\% & 0.535 & 0.731 & 18\% & 0.944 & 0.972 \\
1 July 2021 & 21\% & 1.240 & 1.113 & 14\% & 0.556 & 0.745 & 22\% & 0.863 & 0.929 \\
2 July 2021 & 18\% & 2.364 & 1.538 & 11\% & 0.986 & 0.993 & 7\% & 0.494 & 0.703 \\
3 July 2021 & 36\% & 3.720 & 1.929 & 24\% & 1.230 & 1.109 & 24\% & 1.320 & 1.149 \\ Average & 24\% & 1.33 & 1.15 & 16\% & 0.71 & 0.83 & 20\% & 0.83 & 0.89 \\ \hline \hline \end{tabular}
\end{table}