## 4 Conclusions  
In view of the current problems of complex models and insufficient data processing in the ultra-short-term prediction of photovoltaic power generation, this study constructs and evaluates the performance of a deep learning model based on HPO-KNN-SRU and combining ACF, PACF, and PEARSON theories in ultra-short-term prediction of photovoltaic power. In order to study the predictive performance of the proposed HPO-KNN-SRU model, multiple comparative models were constructed, namely SVR, LSTM, the TCN, and the SRU. At the same time, the HPO-KNN-SRU (only optimizing the KNN), KNN-SRU, and KNN-SVR were constructed for ablation experiments. By analyzing the experimental results, the main conclusions of this paper can be summarized as follows:  
1. The HPO-KNN-SRU model obtained the smallest RMSE and MAE and the largest R2, which shows that the HPO-KNN-SRU model has predictive ability.
2. The prediction accuracy of the dataset model training after KNN outlier processing is further improved, proving the effectiveness of the KNN in processing outliers in photovoltaic power time series data.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & \(P_{RMSE}\) **(\%)** & \(P_{MAE}\) **(\%)** & \(P_{R^{2}}\) **(\%)** \\ \hline HPO-KNN-SRU vs. HPO-KNN-SRU (Only optimize knn) & 0.023 & 2.697 & 0.002 \\ HPO-KNN-SRU (Only optimize knn) vs. KNN-SRU & 0.864 & 1.411 & 0.064 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance metric improvement on the effectiveness of HPO.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & \(P_{RMSE}\) **(\%)** & \(P_{MAE}\) **(\%)** & \(P_{R^{2}}\) **(\%)** \\ \hline KNN-SRU vs. SRU & 16.60 & 21.80 & 1.58 \\ KNN-SVR vs. SVR & 12.56 & 7.83 & 1.47 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance metric improvement on the effectiveness of KNN.  
3. The HPO algorithm applied to optimize the KNN and SRU models can enhance the outlier processing performance of the KNN and the predictive performance of the SRU model. Overall, the proposed HPO-KNN-SRU model can be considered as a competitive technique to improve the ultra-short-term prediction of photovoltaic power. In future work, we will work on optimizing the structure of the SRU network to obtain higher prediction performance. Additionally, in view of the problem that distributed photovoltaics are small in size, there is little historical data in the early stages of construction, and the rapid development of distributed photovoltaics predicts large demand, we plan to introduce incremental learning and transfer learning to expand the application of model algorithms on small sample datasets.  
Conceptualization, Y.T.; methodology, Y.T.; software, Y.T., L.Z. and D.H.; validation, Y.T., D.H. and S.Y.; formal analysis, Y.T.; investigation, Y.T.; resources, Y.T.; data curation, Y.T.; writing--original draft preparation, Y.T.; writing--review and editing, Y.T.; visualization, Y.T.; supervision, Y.T. and Y.K.; project administration, Y.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available at [[https://dkasolarcentre.com.au](https://dkasolarcentre.com.au)]([https://dkasolarcentre.com.au](https://dkasolarcentre.com.au)). These data were derived from the following resources available in the public domain: [[https://dkasolarcentre.com.au/download?location=alice-springs](https://dkasolarcentre.com.au/download?location=alice-springs)]([https://dkasolarcentre.com.au/download?location=alice-springs](https://dkasolarcentre.com.au/download?location=alice-springs)).  
The authors declare no conflicts of interest.