Fig. 1.
H-CNN-BiLSTM network structure.
Fig. 2.
Highway network structure.
Fig. 3.
BiLSTM network structure.
Fig. 4.
Flowchart for error estimation in EV charging pile meters.
Fig. 5.
Topological structure of an electric EV station.
Table 1.
Hyperparameters of H-CNN-BiLSTM.
| Training options | Parameters |
|---|---|
| Optimizer | Adam |
| Mini batch size | 200 |
| Max epochs | 50 |
| Initial learn rate | 0.002 |
| Learn rate drop factor | 0.1 |
| Learn rate drop period | 100 |
Fig. 6.
Model training on the training set.
Fig. 7.
Prediction results of different models on the test set.
Table 2.
Comparison of the performance evaluation indices of the four models.
| Model | R2 | MAE | RMSE |
|---|---|---|---|
| H-CNN-BiLSTM | 0.9771 | 0.1748 | 0.2093 |
| PSO-BPNN | 0.6325 | 0.3626 | 0.4635 |
| EKF-LMRLS | 0.3934 | 0.9417 | 1.1412 |
| GDRLS | 0.2024 | 0.5850 | 1.0166 |
Fig. 8.
Residual box plot of different models.