# Short-Term Forecasting of Power Production in a Large-Scale Photovoltaic Plant Based on LSTM
## 4 Evaluation and Discussions
### Forecasting Result and Discussion  
The training trends of each algorithm are shown in Figure 9 and the error analysis of each algorithm are shown in Table 4, the RMSE and MAPE values for LSTM are respectively 4.07% and 1.52%, while the RMSE and MAPE values for back propagation (BP) networks, least squares support vector machine (LSSVM) and wavelet neural (WN) networks are respectively 16.61% and 2.82%, 16.38% and 3.44%, and 21.29% and 4.83%. The obvious low values indicate that the training result of the LSTM model has a high level of accuracy. Different model structures have been analyzed and the optimal model for each season has been determined in the above. Figure 10b,c are the partial enlarged views of Figure 10a. In Figure 10b, it can be observed that the fitting performance using LSTM is better than using other algorithms with a long \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**BP**} & \multicolumn{2}{c}{**LSSVM**} & \multicolumn{2}{c}{**WNN**} & \multicolumn{2}{c}{**LSTM**} \\ \cline{3-10} \multicolumn{1}{c}{\multirow{-2}{*}{**Season**}} & \multicolumn{1}{c}{**RMSE (\%)**} & \multicolumn{1}{c}{**MAPE (\%)**} & \multicolumn{1}{c}{**RMSE (\%)**} & \multicolumn{1}{c}{**MAPE (\%)**} & \multicolumn{1}{c}{**RMSE (\%)**} & \multicolumn{1}{c}{**MAPE (\%)**} & \multicolumn{1}{c}{**RMSE (\%)**} \\ \hline \multirow{2}{*}{Spring} & training & 17.5\% & 2.86\% & 18.6\% & 3.08\% & 21.02\% & 3.69\% & 5.09\% & 0.82\% \\  & forecasting & 19.6\% & 4.82\% & 20.1\% & 5.37\% & 18.8\% & 4.8\% & 5.34\% & 1.51\% \\ \hline \multirow{2}{*}{Summer} & training & 16.61\% & 2.82\% & 16.38\% & 3.44\% & 21.29\% & 4.83\% & 4.07\% & 1.52\% \\  & forecasting & 13.03\% & 3.18\% & 13.33\% & 2.85\% & 19.05\% & 4.68\% & 9.57\% & 2.01\% \\ \hline \multirow{2}{*}{Autumn}{umnumn}{c}{\multirow{-2}{*}{Autumn}{umnumn}{c}{} & training & 21.91\% & 3.14\% & 21.71\% & 3.28\% & 26.99\% & 4.38\% & 4.96\% & 1.06\% \\  & forecasting & 20.94\% & 2.43\% & 23.11\% & 2.37\% & 23.68\% & 2.79\% & 13.86\% & 1.51\% \\ \hline \multirow{2}{*}{Winter} & training & 24.05\% & 2.45\% & 24.68\% & 2.42\% & 29.85\% & 3.16\% & 3.38\% & 0.38\% \\  & forecasting & 24.68\% & 5.47\% & 17.74\% & 3.69\% & 25.45\% & 6\% & 9.26\% & 1.38\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: RMSE and MAPE of training and forecasting in every season. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**LSTM-Model**} & \multirow{2}{*}{**Learning Rate**} & \multirow{2}{*}{**Optimizer**} & **Layers and** & **Layers and** & \multirow{2}{*}{**Dropout**} \\  & & & **Neurons of** & **Neurons of** & \\  & & & **LSTM** & **Dense** & \\ \hline \#1 (Spring) & 0.01 & ADAM & 3 (10, 10, 10) & 1 (80) & 0.1 \\ \#2 (Summer) & 0.01 & ADAM & 2 (10, 10) & 2 (70, 80) & \(\backslash\) \\ \#3 (Autumn) & 0.15 & ADAM & 2 (10, 10) & 3 (70, 80, 80) & \(\backslash\) \\ \#4 (Winter) & 0.01 & ADAM & 3 (10, 20, 20) & 1 (80) & \(\backslash\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Parameters used for the LSTM in each season. Figure 9: Comparison of training results of different algorithms and actual values in summer (from \(1\) June to \(31\) July \(2017\)). delay in the first six hours. There are some unknown down trend which cannot accurately forecast actual power trends. As shown in Figure 10c, in the case of large weather fluctuations, the power could be effectively predicted by all proposed algorithms, but the forecasting results using LSTM have little error compared to actual power because of the ability of faster perceiving changes from historical data, while the values of prediction using other algorithms are much smaller than the actual value. It can be concluded that the forecasting method using LSTM shows better performance with no delay in sunny days and less error in cloudy day. The same prediction situations appear in the following three figures. Figures 10a, 11, 12 and 13 show forecasting performances for three consecutive days using optimal LSTM model in each season and comparisons with BP networks, LSSVM and WNN networks.