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Research on the Error Estimation Method for Electric Energy Meters of Electric Vehicle Charging Piles based on Deep Learning Cover

Research on the Error Estimation Method for Electric Energy Meters of Electric Vehicle Charging Piles based on Deep Learning

Open Access
|Apr 2025

Figures & Tables

Fig. 1.

H-CNN-BiLSTM network structure.
H-CNN-BiLSTM network structure.

Fig. 2.

Highway network structure.
Highway network structure.

Fig. 3.

BiLSTM network structure.
BiLSTM network structure.

Fig. 4.

Flowchart for error estimation in EV charging pile meters.
Flowchart for error estimation in EV charging pile meters.

Fig. 5.

Topological structure of an electric EV station.
Topological structure of an electric EV station.

Fig. 6.

Model training on the training set.
Model training on the training set.

Fig. 7.

Prediction results of different models on the test set.
Prediction results of different models on the test set.

Fig. 8.

Residual box plot of different models.
Residual box plot of different models.

Comparison of the performance evaluation indices of the four models_

ModelR2MAERMSE
H-CNN-BiLSTM0.97710.17480.2093
PSO-BPNN0.63250.36260.4635
EKF-LMRLS0.39340.94171.1412
GDRLS0.20240.58501.0166

Hyperparameters of H-CNN-BiLSTM_

Training optionsParameters
OptimizerAdam
Mini batch size200
Max epochs50
Initial learn rate0.002
Learn rate drop factor0.1
Learn rate drop period100
Language: English
Page range: 40 - 47
Submitted on: May 10, 2024
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Accepted on: Mar 3, 2025
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Published on: Apr 15, 2025
In partnership with: Paradigm Publishing Services
Publication frequency: Volume open

© 2025 Juan Wang, Wei Liu, Yong Zhang, Zhi Liu, Xiaolei Zheng, Yuxin Wang, Jianshu Hao, Xuanding Dai, published by Slovak Academy of Sciences, Institute of Measurement Science
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.