## 5 Conclusions  
This paper proposes a multimodal deep learning method for forecasting the daily power generation of small hydropower stations considering the temporal and spatial distribution of precipitation. The characteristics of the method are as follows:  
1. Precipitation grid data with spatial distribution differences are applied to the neural network model to deeply explore the influence of the spatial distribution of precipitation on the daily power generation of a group of small hydropower stations. 2. The time lag between daily power generation changes and precipitation is analyzed, and the PMI method is used to estimate this \"time difference\", which is used to select the best time-scale precipitation data for forecasting the daily power generation of a group of small hydropower stations. 3. Using multimodal deep learning methods based on a CNN and an MLP, according to the different characteristics of each modal data, different methods can be used to fully extract and integrate the characteristic information hidden in the precipitation and historical power generation data, improving the accuracy of forecasts of the daily power generation of a group of small hydropower stations. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Evaluation**} & \multicolumn{6}{c}{**Methods**} \\ \cline{2-9}
**Metrics** & **SVR** & **GBRT** & **RF** & **LSTM** & **MLP** & **CNN** & **CM-MDLN** \\ \hline _AC_ (\%) & 87.31 & 86.77 & 87.97 & 84.91 & 86.32 & 85.87 & 93.07 \\ _MAPE_ (\%) & 8.70 & 9.01 & 8.30 & 10.72 & 9.92 & 9.16 & 5.71 \\ _RMSE_ (MWh) & 326.28 & 277.51 & 267.66 & 418.31 & 313.40 & 279.93 & 238.41 \\ \(R^{2}\) (\(\times 10^{-1}\)) & 9.613 & 9.692 & 9.607 & 9.504 & 9.635 & 9.689 & 9.847 \\ _APE_ \(<10\%\) (d)\({}^{\ddagger}\) & 74 & 80 & 78 & 64 & 70 & 74 & 92 \\ _APE_ \(<10\%\) (\(\%\))\({}^{\ddagger}\) & 69.81 & 75.47 & 73.58 & 60.38 & 66.04 & 69.81 & 86.79 \\ _APE_ \(<5\%\) (d)\({}^{\ddagger}\) & 44 & 46 & 47 & 37 & 30 & 45 & 55 \\ _APE_ \(<5\%\) (\(\%\)) \({}^{\ddagger}\) & 41.51 & 43.39 & 44.34 & 34.91 & 28.30 & 42.45 & 51.89 \\ \hline \hline \end{tabular} \({}^{\ddagger}\) The number of days with APE less than 10\(\%\). \({}^{\ddagger}\) The percentage of days with APE less than 10\(\%\). \({}^{\ddagger}\) The number of days with APE less than 5\(\%\). \({}^{\ddagger}\) The percentage of days with APE less than 5\(\%\).  
\end{table} 
Table 4: Statistics of evaluation metrics for forecasting results of different methods in the HC city testing set. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Evaluation**} & \multicolumn{6}{c}{**Methods**} \\ \cline{2-9}
**Metrics** & **SVR** & **GBRT** & **RF** & **LSTM** & **MLP** & **CNN** & **CM-MDLN** \\ \hline _AC_ (\%) & 88.70 & 89.60 & 89.72 & 84.78 & 85.79 & 86.51 & 92.80 \\ _MAPE_ (\%) & 7.45 & 7.52 & 7.40 & 11.59 & 9.57 & 10.33 & 5.70 \\ _RMSE_ (MWh) & 305.29 & 290.99 & 311.98 & 429.62 & 344.93 & 362.73 & 210.63 \\ \(R\) (\(\times 10^{-1}\)) & 9.714 & 9.740 & 9.702 & 9.435 & 9.635 & 9.597 & 9.808 \\ _APE_ \(<10\%\) (d)\({}^{\ddagger}\) & 80 & 79 & 79 & 60 & 70 & 64 & 82 \\ _APE_ \(<10\%\) (\(\%\))\({}^{\ddagger}\) & 75.47 & 74.52 & 74.52 & 56.60 & 66.03 & 60.37 & 77.36 \\ _APE_ \(<5\%\) (d)\({}^{\ddagger}\) & 51 & 47 & 52 & 33 & 43 & 35 & 56 \\ _APE_ \(<5\%\) (\(\%\)) \({}^{\ddagger}\) & 48.11 & 44.33 & 49.05 & 31.13 & 40.56 & 33.01 & 52.83 \\ \hline \hline \end{tabular} \({}^{\ddagger}\) The number of days with APE less than 10\(\%\). \({}^{\ddagger}\) The percentage of days with APE less than 10\(\%\). \({}^{\ddagger}\) The number of days with APE less than 5\(\%\). \({}^{\ddagger}\) The percentage of days with APE less than 5\(\%\).  
\end{table} 
Table 5: Statistics of evaluation metrics for forecasting results of different methods in the GL city testing set.