## 7 Conclusions  
Photovoltaic (PV) power generation is greatly affected by external factors, and the output power is random and unstable. In order to improve the prediction accuracy of PV power generation and reduce the impact of PV grid connection on the power system, a new MISAO-ICEEMDAN-WLSSVM prediction model was established. The research results are as follows: \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Models** & **MAPE** & **MAE** & **RMSE** \\ \hline TVF-EMD-ELM (M1) & 1.9720 & 13.2230 & 1.4455 \\ WPD-LFABS-SDS-GRU (M2) & 1.4954 & 9.2411 & 0.9537 \\ SSA-VMD-Informer (M3) & 1.7036 & 11.1997 & 1.2406 \\ Proposed model & **1.0999** & **7.0049** & **0.6970** \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison with three existing models. \begin{table}
\begin{tabular}{l l} \hline \hline
**Model** & **Description** \\ \hline TVF-EMD-ELM (M1) [48] & The TVF-EMD method decomposes the PV generation data into more stable and constant subsequences. A specially specified set of features (Intrinsic Mode Function (IMF)) is used to train and improve the forecasting Extreme Learning Machine (ELM) model. The modified ELM model is used to evaluate the forecasting effectiveness. \\ \hline WPD-LFABS-SDS-GRU (M2) [30] & Wavelet packet decomposition extracts the low and high-frequency components hidden in the PV power. The SDS method is combined with the LFBAS optimization algorithm to screen for days similar to the forecast day. A series of GRU networks optimized by LFBAS are used to predict the sub-signals after WPD decomposition and synthesize the final PV power forecast. \\ \hline SSA-VMD-Informer (M3) [49] & Firstly, the temporal coding of the Informer model is improved; secondly, the original series is decomposed into multiple modal components using VMD; then, the results of VMD are optimized in conjunction with the optimization strategy of SSA to improve the characteristics of the time series data. Finally, the refined data are input into the Informer framework for modeling and prediction. \begin{table}
\begin{tabular}{l l} \hline \hline
**Model** & **Description** \\ \hline \multirow{4}{*}{TVF-EMD-ELM (M1) [48]} & The TVF-EMD method decomposes the PV generation data into more stable and constant subsequences. A specially specified set of features (Intrinsic Mode Function (IMF)) is used to train and improve the forecasting Extreme Learning Machine (ELM) model. The modified ELM model is used to evaluate the forecasting effectiveness. \\ \hline WPD-LFABS-SDS-GRU (M2) [30] & Wavelet packet decomposition extracts the low and high-frequency components hidden in the PV power. The SDS method is combined with the LFBAS optimization algorithm to screen for days similar to the forecast day. A series of GRU networks optimized by LFBAS are used to predict the sub-signals after WPD decomposition and synthesize the final PV power forecast. \\ \hline SSA-VMD-Informer (M3) [49] & Firstly, the temporal coding of the Informer model is improved; secondly, the original series is decomposed into multiple modal components using VMD; then, the results of VMD are optimized in conjunction with the optimization strategy of SSA to improve the characteristics of the time series data. Finally, the refined data are input into the Informer framework for modeling and prediction. \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of the prediction results between the existing models and proposed model. 1. A MISAO algorithm is proposed by improving the SAO optimization algorithm in three aspects. A series of numerical experiments were conducted using 30 CEC 2017 benchmark functions, and the results show that the MISAO algorithm has a better ability to find the optimal solution and jump out of the local optimal solution. 2. The MISAO algorithm was used to optimize the parameters of the ICEEMDAN-WLSSVM model, and the MISAO-ICEEMDAN-WLSSVM prediction model was established. 3.