Research on Error Estimation Method of Electric Vehicle Charging Meter Based on Spatio-temporal network
DAI Xuanding1, HE Yuchen2, QIAN Lijuan2,3, ZHANG Huanghui4, SHAO Haiming5, LIN Guoqiang6, LIN Qiang6
1.College of Metrology Measurement and Instrument， China Jiliang University， Hangzhou， Zhejiang 310018， China
2.College of Mechanical and Electrical Engineering， China Jiliang University， Hangzhou， Zhejiang 310018， China
3.Innovation Center of Yangtze River Delta，Zhejiang University， Jiaxing， Zhejiang 314102， China
4.Fujian Metrology Institute，Fuzhou，Fujian 350001，China
5.National Institute of Metrology，Beijing 100029， China
6.Fujian Minliang Calibration Technology Center Co. Ltd.， Fuzhou， Fujian 350003， China
Abstract：The performance of smart meters usually declines with time， and on-site verification requires a lot of manpower and material resources. Therefore， an error estimation method based on the combination of high-speed convolutional neural network（CNN） and bidirectional long short-term memory（BiLSTM） network is proposed. Firstly， the data characteristics of charging facilities collected by smart meters are preprocessed. Secondly， the spatial features between variables are extracted based on the convolution module added to the high-speed network， and part of the original information is retained. Finally， the performance of the proposed method is verified in the data set of an energy vehicle charging station in a certain place. Experimental results show that the proposed method outperforms the other state-of-the-art methods in error analysis of electric meters. The experimental results show that the proposed method has significant advantages in error estimation accuracy of charging meters， and the three performance evaluation indicators have at least 13.68% improvement.
戴煊丁, 何雨辰, 钱丽娟, 张煌辉, 邵海明, 刘国强, 林强. 基于时空网络的电动汽车充电桩电能表误差估计方法研究[J]. 计量学报, 2025, 46(1): 126-132.
DAI Xuanding, HE Yuchen, QIAN Lijuan, ZHANG Huanghui, SHAO Haiming, LIN Guoqiang, LIN Qiang. Research on Error Estimation Method of Electric Vehicle Charging Meter Based on Spatio-temporal network. Acta Metrologica Sinica, 2025, 46(1): 126-132.
张国荣，陈夏冉.能源互联网未来发展综述［J］.电力自动化设备，2017， 37（1）： 1-7. ZHANG G R， CHEN X R. Future Development of Energy Internet［J］. Electric Power Automation Equipment，2017， 37（1）： 1-7.
14
XIA T， LIU C C， LEI M， et al. Measurement Error Estimation for Distributed Smart Meters Through a Modified BP Neural Network［J］. Frontiers in Energy Research， 2022.
3
陈昊，乔亚男，刘婧，等.考虑模型病态性的智能电表运行误差分析方法［J］.电力建设，2020， 41（2）： 94-100. CHEN H， QIAO Y N， LIU J， et al. Analysis Method for Operation Error of Smart Meter Considering Ill-Conditioned Model［J］. Electric Power Construction， 2020， 41（2）： 94-100.
9
马玉莹. 基于量测数据分析的智能电表运行误差远程估计［D］. 天津：天津大学， 2019.
10
XIE W W， ZHANG L P， ZHANG B S， et al. Reliability Analysis of Intelligent Electric Energy Meter under Fusion Model Illness Analysis Algorithm［J］. Journal of Sensors， 2021.
13
刘文宇， 刘璐， 刘馨然， 等. 基于改进BP神经网络的低压配电台区智能电能表误差状态评估模型［J］. 电测与仪表， 2022， 59（11）： 176-181. LIU W Y， LIU L， LIU X R， et al. An Error Estimation Method of Smart Electricity Meter in Low Voltage Distribution Area Based on Improved BP Neural Network［J］. Electrical Measurement & Instrumentation， 2022， 59（11）： 176-181.
2
徐锦涛，冯兴乐，赵峰.智能电表可靠性预计技术研究［J］.智慧电力，2018， 46（4）： 28-32. XU J T， FEN X L， ZHAO F. Study on Intelligent Electric Energy Meter Reliability Prediction［J］. Smart Power， 2018， 46（4）： 28-32.
12
庹璟， 唐登平， 蔡文嘉， 等. 基于状态参量的智能电表误差状态预测方法［J］. 仪表技术， 2019， 359（3）： 1-5. TUO J， TANG D P， CAI W J， et al. An Error State Forecasting Method for Smart Meters Based on State Parameters［J］. Instrumentation Technology， 2019， 359（3）： 1-5.
5
刁瑞朋，王强，卢志俊. 零线断接对三相四线电能表计量影响机理分析［J］.计量学报，2023， 44（5）： 750-757. DIAO R P， WANG Q， LU X J. Mechanism of the Disconnection of the Neutral Wire on the Measurement of the Three-phase Four-wire Electric Energy Meter［J］. Acta Metrologica Sinica， 2023， 44（5）： 750-757.
15
卢国波. 基于智能电表数据分析的精细化时-空负荷特性分析［D］. 吉林：东北电力大学， 2017.
1
史鹏博， 李蕊， 李铭凯， 等. 基于决策树和聚类算法的智能电表误差估计与故障检测［J］. 计量学报，2022， 43（8）： 1089-1094. SHI P B， LI R， LI M K， et al. Error Estimation and Fault Detection of Smart Meter Based on DecisionTree and Clustering Algorithm［J］. Acta Metrologica Sinica， 2022， 43（8）： 1089-1094.
17
罗顺桦， 王振雷， 王昕. 基于注意力机制的Multi-head CNN-LSTM软测量建模［J］. 控制工程，2022， 29（10）： 1821-1828. LUO S Y， WANG Z L， WANG X. Multi-head CNN-LSTM Soft Sensing Modeling Based on Attention Mechanism［J］. Control Engineering of China， 2022， 29（10）： 1821-1828.
11
KONG X Y， ZHANG X P， LU N， et al. Online Smart Meter Measurement Error Estimation Based on EKF and LMRLS Method［J］. IEEE Transactions on Smart Grid， 2021， 12（5）： 4269-4279.
7
NAKUTIS Z， KASKONAS P， SAUNORIS M， et al. A Framework for Remote In-service Metrological Surveillance of Energy Meters［J］. Measurement， 2021， 168
8
高磊. 智能电能表计量装置运行误差分析及状态评价方法研究［J］. 电子测试， 2018（12）： 104-105. GAO L. Research on Operational Error Analysis and State Evaluation Method of Intelligent Electric Energy Meter Metering Device［J］. Electronic Test， 2018（12）： 104-105.
6
鞠雯. 市场监管总局调整实施强制管理的计量器具目录［J］. 中国计量， 2020（12）： 6. JU W. The State Administration for Market Regulation adjusts the catalog of measuring instruments subject to mandatory management［J］. China Metrology， 2020（12）： 6.
16
LU X Y， LIN Y H， LIN P J， et al. Efficient Fault Diagnosis Approach for Solar Photovoltaic Array Using a Convolutional Neural Network in Combination of Generative Adversarial Network under Small Dataset［J］. Solar Energy， 2023， 253： 360-374.
2025, Vol. 46 
