Figure 12 Ablation test performance comparison chart
Analyzing the comparison between Table 7 and Figure 11,12 the TPE-CBiGRU0 model compared to the TPE-CBiGRU1 model, due to the removal of data layer feature mining input, the TPE-CBiGRU0 model lacks key feature perception of time scale and power cycle scale in the data, resulting in a significant decrease in model performance. Compared to the TPE-CBiGRU1 model, the MAE and RMSE of the TPE-CBiGRU0 model increased by 32.55% and 50.63%, respectively, indicating a significant increase in errors, with a decrease in the coefficient of determination (R²) by 4.63%. The poor fit between the curves of TPE-CBiGRU0 and TPE-CBiGRU1 in Figure 11 also confirms this, indicating that data layer feature fusion can effectively extract multi-scale features in the data, thereby improving model prediction accuracy.
Comparing the TPE-BiGRU model with the TPE-CBiGRU1 model, the performance of the model decreases when the spatial features extracted from the data by the CNN network are missing. By contrasting Figure 12, the MAE and RMSE increase by 4.89% and 18.54%, respectively, indicating an increase in errors, with a decrease in the coefficient of determination (R²) by 1.49%. Combined with Figure 11, the comparison indicates that the presence of the CNN network can improve the smoothness of the prediction curve, and prediction models with CNN tend to fit the real power curve better than models without CNN. The spatial features extracted by the CNN network from the data have a beneficial effect on the proposed model. Comparing the TPE-CNN model with the TPE-CBiGRU1 model, the model’s performance decreases significantly when lacking the temporal feature information extracted from long-time series data by the Bi-GRU network. The MAE and RMSE increase by 49.99% and 40.89%, respectively, indicating a significant increase in errors, with a decrease in the coefficient of determination (R²) by 3.60%. This demonstrates the critical role of the Bi-GRU network in extracting temporal features, especially for long sequences. Through the comparison between the TPE-CBiGRU1 model and the TPE-BiGRU, TPE-CNN models, it is evident that the Add layer, which fuses the spatial-temporal feature matrices extracted by the TPE-BiGRU and TPE-CNN networks, effectively mines the crucial spatial-temporal features of the data, significantly reducing model errors and further improving the model’s fit.

## Conclusion

This study addresses the challenge of improving the prediction accuracy of distributed photovoltaic (PV) power generation. Considering the multidimensional features and predictive performance, a short-term PV power generation prediction model based on multiscale feature fusion TPE-CBiGRU hybrid network is proposed. This model utilizes data-level fusion to construct the input feature information of the model and model-level feature fusion to extract high-dimensional spatial-temporal features from the CBiGRU network. By integrating the TPE Bayesian hyperparameter automatic optimization method, the model further enhances the accuracy of PV power prediction.
The main conclusions of this study are as follows:
The proposed TPE-based Bayesian hyperparameter automatic optimization method effectively searches for the optimal parameter combination of the model. Compared to manual parameter adjustment methods, the computational experiments consumed only 21 hours, 35 minutes, and 55 seconds. The model’s training and validation set MAE iterated rapidly, remaining below 0.1. The highest validation accuracy reached 96.00%, significantly enhancing the model’s fitting capability and improving the efficiency of parameter adjustment.
Data-level fusion provides crucial features for the TPE-CBiGRU model, while model-level fusion combines the advantages of CNN spatial feature extraction and BiGRU model’s temporal prediction, enabling deep exploration of the correlation between data and temporal output, thereby enhancing the accuracy of temporal prediction. The results indicate that the TPE-CBiGRU model outperforms the other five models in evaluation metrics. Compared to the GRU model, the MAE and RMSE of the TPE-CBiGRU model decrease significantly by 26.11% and 35.64%, respectively, while R2 increases by 3.07%.

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