# Assessment of Different Deep Learning Methods of Power Generation Forecasting for Solar PV System
## 5 Numerical Results
### Forecast Performance with Different Weather Data Groups  
After deep learning model training, these models were applied to execute solar photovoltaic forecasting from 27 June to 3 July 2021. This section describes the use of different deep learning methods (ANN, LSTM, and GRU) with different weather groups for solar photovoltaic forecasting. The forecasting results of the ANN model are presented in Figure 7. The solid blue line is the actual PV power generation, the green dashed line is the predicted result using LWS weather data, the red dashed line is the predicted result using hybrid weather data, and the yellow dashed line is the predicted result using CWB weather data. The lines used in the other graphs have the same meanings. It is apparent that when only CWB data are used, the prediction results of the ANN model severely deviate from the actual power generation during the peak period on 27 June and 29 June 2021. The results indicate that the ANN model does not provide good predictions, even under sunny conditions. On the other hand, when only historical LWS data are used, the results during \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Input Time**} & \multirow{2}{*}{**Layers**} & \multirow{2}{*}{**Epochs**} &
\begin{tabular}{c} **Learning** \\ **Rate** \\ \end{tabular} & \multirow{2}{*}{**Batch Size**} & \multirow{2}{*}{**MAE**} & \multirow{2}{*}{**MAPE**} & \multirow{2}{*}{**RMSE**} \\ \hline LSTM & 2 days & 3 & 2000 & \(1\times 10^{-3}\) & 8 & 0.5074 & 17\% & 0.9979 \\ GRU & 2 days & 3 & 2000 & \(1\times 10^{-3}\) & 8 & 0.4898 & 18\% & 0.8397 \\ ANN & 1 day & 3 & 2000 & \(1\times 10^{-3}\) & 8 & 0.8999 & 34\% & 1.6067 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The hyperparameter tuning results of LWS historical data. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Input Time**} & \multirow{2}{*}{**Layers**} & \multirow{2}{*}{**Epochs**} &
\begin{tabular}{c} **Learning** \\ **Rate** \\ \end{tabular} & \multirow{2}{*}{**Batch Size**} & \multirow{2}{*}{**MAE**} & \multirow{2}{*}{**MAPE**} & \multirow{2}{*}{**RMSE**} \\ \hline LSTM & 1 day & 4 & 2000 & \(1\times 10^{-3}\) & 8 & 0.31242 & 9\% & 0.5921 \\ GRU & 1 day & 4 & 2000 & \(1\times 10^{-3}\) & 8 & 0.36907 & 11\% & 0.6911 \\ ANN & 2 days & 5 & 2000 & \(1\times 10^{-3}\) & 8 & 0.35434 & 10\% & 1.3326 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The hyperparameter tuning results of hybrid data. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Input Time**} & \multirow{2}{*}{**Layers**} & \multirow{2}{*}{**Epochs**} &
\begin{tabular}{c} **Learning** \\ **Rate** \\ \end{tabular} & \multirow{2}{*}{**Batch Size**} & \multirow{2}{*}{**MAE**} & \multirow{2}{*}{**MAPE**} & \multirow{2}{*}{**RMSE**} \\ \hline LSTM & 2 days & 3 & 2000 & \(1\times 10^{-3}\) & 8 & 0.5074 & 17\% & 0.9979 \\ GRU & 2 days & 3 & 2000 & \(1\times 10^{-3}\) & 8 & 0.4898 & 18\% & 0.8397 \\ ANN & 3 days & 5 & 2000 & \(1\times 10^{-3}\) & 8 & 0.8999 & 34\% & 1.6067 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The hyperparameter tuning results of CWB data. peak power generation periods deviate quite substantially from the actual values due to poor weather (e.g., 28 June to 29 June). In contrast, the overall prediction curve using hybrid data is closer to the actual power generation trend, although the results are still not good enough under poor weather conditions. The forecasting results of the LSTM model are shown in Figure 8. When CWB data are used for prediction, the accuracy of LSTM is better than that of ANN. This means that the learning mode with time series helps to improve the predictive accuracy. However, from the prediction results of LWS data, it is found that although the results are better than those of the ANN model (the trends on 28 and 29 June 2021 show significant improvement), there is still a big difference in the prediction results on 1 July 2021.