## 7 Conclusions
The MISAO-ICEEMDAN-WLSSVM model was validated using data from a photovoltaic (PV) power plant in Hebei Province, China, and the prediction results were compared with seven neural network models, including WLSSVM, ICEEMDAN-WLSSVM, and MISAO-ICEEMDAN-WLSSVM, for three different weather types. The results show that the prediction curve of the MISAO-ICEEMDAN-WLSSVM model is closer to the actual value curve with the smallest error. 4. Three evaluation indexes, MAPE, MAE and RMSE, were used to evaluate the seven models. The MAPE values of the proposed model were reduced by at least 25.3%, 29.1% and 32.8%; the MAE values were reduced by at least 39.0%, 17.8% and 32.8%; and the RMSE values were reduced by at least 37.2%, 16.0% and 13.3%, respectively, under the three types of weather compared with the other six models. The results showed that the MISAO-ICEEMDAN-WLSSVM prediction model has high prediction accuracy and stability. The proposed prediction model is conducive to accurately predicting the PV output power, making full use of solar energy resources, reducing the impact of the grid-connected PV on the modern power grid power system, promoting the smooth operation of grid scheduling, and maintaining the security and stability of the power system. Furthermore, there are certain limitations in the prediction aspect of this study. Currently, the one-step forecasting method is employed. One-step forecasting restricts the comprehensive understanding of the long-term variation trend of photovoltaic power and may not fully meet the precise prediction requirements for photovoltaic power changes over a more extended period in some complex scenarios. Future research efforts will be dedicated to exploring multi-step forecasting methods. Intensive study will advance prediction techniques and model architectures, integrate more valuable feature information, and optimize the model training strategies to enhance the accuracy and stability of multi-step forecasting, providing more reliable technical support for optimized management and efficient operation in the field of new energy. The limitations of this study also include the varying prediction effects of the model under different weather types. Specifically, when it is cloudy and rainy, the forecasting effect is less than when it is sunny. A thorough analysis of how to enhance the impact of weather type on forecasting has yet to be conducted. Moreover, compared to periods of high-power generation, the prediction accuracy is lower in the morning or evening. It is necessary to investigate the enhancement of the forecasting efficacy throughout the day. Future research should examine the effects of different weather conditions on the generation of PV power in more detail. Conceptualization, X.Z. and J.Y. ; methodology, J.Y. ; software, J.Y., L.G. and S.M. ; validation, H.H. and Q.X. ; formal analysis, J.Y. ; investigation, J.Y., L.G. and S.M. ; resources, J.Y. ; data curation, H.H. and Q.X. ; writing--original draft preparation, J.Y. ; writing--review and editing, X.Z. ; visualization, J.Y., L.G., S.M., H.H. and Q.X. ; supervision, X.Z. ; project administration, J.Y. ; funding acquisition, X.Z. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Natural Science Foundation of China (52107135) and the Fundamental Research Funds for the Central Universities (2024 ZKPYJD07). Not applicable. Data Availability Statement: The required datasets for this experiment can be obtained for free from [[https://www.scidb.cn/en/detail?dataSetId=f8f3d7](https://www.scidb.cn/en/detail?dataSetId=f8f3d7) af144f441795c5781497e50b62#p2]([https://www.scidb.cn/en/detail?dataSetId=f8f3d7](https://www.scidb.cn/en/detail?dataSetId=f8f3d7) af144f441795c5781497e50b62#p2) (accessed on 2 January 2024). We thank the National Natural Science Foundation of China (52107135) and the Fundamental Research Funds for the Central Universities (2024 ZKPYJD07) for providing financial support for this study. The authors declare no conflicts of interest.