Figure 10 Power prediction curve (a) (c) Sunny (b) (d) Cloudy
Combining Table 6 and Figure 10, a comparative analysis of the performance of each model was conducted. Overall, the TPE-CBiGRU model achieved the best performance in terms of mean absolute error (MAE) and root mean square error (RMSE), with the highest coefficient of determination (R²). The C-LSTM and Bi-GRU models followed in performance. The proposed TPE-CBiGRU model excelled across multiple metrics, demonstrating the highest accuracy and the closest fit of the prediction curve to the actual curve.
Specifically, compared to traditional deep learning models, the proposed TPE-CBiGRU model shows a reduction in mean absolute error (MAE) by 4.89% and 9.22% compared to the Bi-GRU and LSTM models, respectively. The reductions are even more pronounced for the Bi-LSTM, GRU, and C-LSTM models, at 13.62%, 26.11%, and 19.34%, respectively, indicating an improvement in model accuracy. In terms of root mean square error (RMSE), the TPE-CBiGRU model exhibits a significant reduction of 18.54% and 16.97% compared to the Bi-GRU and C-LSTM models, respectively. The largest reductions are observed when compared to the Bi-LSTM, GRU, and LSTM models, at 34.70%, 35.64%, and 25.34%, respectively.From Figure 10, under sunny conditions (a) and (c), the TPE-CBiGRU model’s prediction curve fits well with the actual data, with the other models performing slightly worse. This trend is even more evident under cloudy conditions (b) and (d), highlighting the advantage of lower errors in the proposed model. Regarding the coefficient of determination (R²), the Bi-GRU and C-LSTM models perform closely to the TPE-CBiGRU model, with R² values of 94.54% and 94.68%, respectively. The lowest R² values are observed for the Bi-LSTM, GRU, and LSTM models, at 93.05%, 92.96%, and 93.94%, respectively. This demonstrates that the feature fusion component of the model effectively integrates the spatial and temporal features extracted by CNN and Bi-GRU, enhancing the model’s prediction accuracy.
Furthermore, comparing the conditions depicted in Figure 10 (a) and (c) under sunny conditions with (b) and (d) under cloudy conditions, the following observations can be made: under sunny conditions, all six predictive models demonstrate relatively high prediction accuracy, with minimal performance differences among them. In sunny conditions, the photovoltaic power exhibits a relatively stable curve with minimal fluctuations. Therefore, each model only needs to focus on the overall trend and offset of the prediction curve without considering subtle fluctuations extensively. This simplifies the prediction task as the models only need to capture the general shape of the curve without dealing with complex fluctuations. In contrast, under cloudy conditions, the proposed TPE-CBiGRU model exhibits the highest prediction accuracy. Although other models also demonstrate high accuracy in sunny conditions, albeit slightly lower than the TPE-CBiGRU model, considering the predictive performance across various scenarios, the model proposed in this study shows superior performance.
In summary, the fusion of features at the data layer to extract temporal and power cycle characteristics and construct input feature matrices can effectively enhance prediction accuracy. Simultaneously, feature fusion at the model layer effectively integrates high-dimensional spatiotemporal features extracted by CNN and Bi-GRU networks. This further explores the mapping relationship between input features and photovoltaic power, thereby improving the model’s predictive accuracy. As a result, the predicted power variations closely align with the actual power variations, demonstrating a consistent trend.
Ablation Experiments
To further validate the effectiveness of each component of the TPE-CBiGRU model, ablation experiments were conducted for analysis. By reducing components, the contribution of each component to the model performance was evaluated. There are three scenarios in total: (a) TPE-CBiGRU0 model only inputs raw feature data without data layer feature mining, verifying the effectiveness of data layer fusion; (b) TPE-BiGRU model inputs raw data and data layer feature mining features, validating CNN’s ability to extract spatial features; (c) TPE-CNN model inputs raw data and data layer feature mining features, validating the feature extraction ability of the Bi-GRU network for time-series data. The TPE-CBiGRU1 model proposed in Section 2 was established as a comparison to test the effectiveness of the data layer feature fusion component and the model layer feature fusion component. The missing modules in (b) and (c) were replaced by Dense with the same parameters. The experimental comparison results are shown in Table 7, and the power prediction curves of each component model are compared in Figure 11, with the prediction date selected as February 16, 2023, from 0:00 to 24:00.
Table 7 Comparison of ablation experiments