Real-Time Monitoring and Simulation of Multi-User Electricity Metering Anomaly Data Based on Distributed System
Amidst the rapid development of smart grids and distributed energy systems, the volume and complexity of data within power systems have significantly increased, posing substantial challenges to traditional anomaly detection methods. To overcome these challenges, this study introduces a V-LSTM framew...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10930424/ |
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| author | Kai Liu Xuchao Jia Junlong Wang Xun Ma Jiadong Li |
| author_facet | Kai Liu Xuchao Jia Junlong Wang Xun Ma Jiadong Li |
| author_sort | Kai Liu |
| collection | DOAJ |
| description | Amidst the rapid development of smart grids and distributed energy systems, the volume and complexity of data within power systems have significantly increased, posing substantial challenges to traditional anomaly detection methods. To overcome these challenges, this study introduces a V-LSTM framework, an innovative approach that combines a variational autoencoder (VAE) with a long short-term memory (LSTM) network for anomaly detection in distributed power metering systems. The framework facilitates the extraction of features from multi-source data, generated by users’ electricity consumption behavior, via VAE, while LSTM conducts a meticulous time-series analysis of these features to enable high-precision anomaly detection in complex datasets. VAE proficiently mitigates data complexity, preserving essential anomalous information, whereas LSTM augments the model’s capacity to manage temporal dependencies. In empirical evaluations utilizing both public and proprietary datasets, the V-LSTM framework demonstrates superior performance in accuracy and AUC metrics, decisively surpassing traditional detection approaches. The experimental findings indicate that V-LSTM not only enhances the accuracy and dependability of power anomaly detection but also offers a novel technical trajectory for anomaly monitoring within the smart grid. This research outcome fosters the continued evolution of smart grid technology, providing more scientifically grounded management and decision support for power utilities and energy management entities. |
| format | Article |
| id | doaj-art-32a24b9c2aa84a8a926250a30ffa3fd1 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-32a24b9c2aa84a8a926250a30ffa3fd12025-08-20T03:42:18ZengIEEEIEEE Access2169-35362025-01-0113508765088410.1109/ACCESS.2025.355206210930424Real-Time Monitoring and Simulation of Multi-User Electricity Metering Anomaly Data Based on Distributed SystemKai Liu0https://orcid.org/0009-0009-9616-925XXuchao Jia1https://orcid.org/0009-0004-6538-1200Junlong Wang2Xun Ma3Jiadong Li4https://orcid.org/0009-0004-3262-2047Marketing Service Center, State Grid Hebei Electric Power Company Ltd., Shijiazhuang, ChinaMarketing Service Center, State Grid Hebei Electric Power Company Ltd., Shijiazhuang, ChinaMarketing Service Center, State Grid Hebei Electric Power Company Ltd., Shijiazhuang, ChinaMarketing Service Center, State Grid Hebei Electric Power Company Ltd., Shijiazhuang, ChinaMarketing Service Center, State Grid Hebei Electric Power Company Ltd., Shijiazhuang, ChinaAmidst the rapid development of smart grids and distributed energy systems, the volume and complexity of data within power systems have significantly increased, posing substantial challenges to traditional anomaly detection methods. To overcome these challenges, this study introduces a V-LSTM framework, an innovative approach that combines a variational autoencoder (VAE) with a long short-term memory (LSTM) network for anomaly detection in distributed power metering systems. The framework facilitates the extraction of features from multi-source data, generated by users’ electricity consumption behavior, via VAE, while LSTM conducts a meticulous time-series analysis of these features to enable high-precision anomaly detection in complex datasets. VAE proficiently mitigates data complexity, preserving essential anomalous information, whereas LSTM augments the model’s capacity to manage temporal dependencies. In empirical evaluations utilizing both public and proprietary datasets, the V-LSTM framework demonstrates superior performance in accuracy and AUC metrics, decisively surpassing traditional detection approaches. The experimental findings indicate that V-LSTM not only enhances the accuracy and dependability of power anomaly detection but also offers a novel technical trajectory for anomaly monitoring within the smart grid. This research outcome fosters the continued evolution of smart grid technology, providing more scientifically grounded management and decision support for power utilities and energy management entities.https://ieeexplore.ieee.org/document/10930424/Anomaly detectionelectricity meteringVAELSTMdeep learning |
| spellingShingle | Kai Liu Xuchao Jia Junlong Wang Xun Ma Jiadong Li Real-Time Monitoring and Simulation of Multi-User Electricity Metering Anomaly Data Based on Distributed System IEEE Access Anomaly detection electricity metering VAE LSTM deep learning |
| title | Real-Time Monitoring and Simulation of Multi-User Electricity Metering Anomaly Data Based on Distributed System |
| title_full | Real-Time Monitoring and Simulation of Multi-User Electricity Metering Anomaly Data Based on Distributed System |
| title_fullStr | Real-Time Monitoring and Simulation of Multi-User Electricity Metering Anomaly Data Based on Distributed System |
| title_full_unstemmed | Real-Time Monitoring and Simulation of Multi-User Electricity Metering Anomaly Data Based on Distributed System |
| title_short | Real-Time Monitoring and Simulation of Multi-User Electricity Metering Anomaly Data Based on Distributed System |
| title_sort | real time monitoring and simulation of multi user electricity metering anomaly data based on distributed system |
| topic | Anomaly detection electricity metering VAE LSTM deep learning |
| url | https://ieeexplore.ieee.org/document/10930424/ |
| work_keys_str_mv | AT kailiu realtimemonitoringandsimulationofmultiuserelectricitymeteringanomalydatabasedondistributedsystem AT xuchaojia realtimemonitoringandsimulationofmultiuserelectricitymeteringanomalydatabasedondistributedsystem AT junlongwang realtimemonitoringandsimulationofmultiuserelectricitymeteringanomalydatabasedondistributedsystem AT xunma realtimemonitoringandsimulationofmultiuserelectricitymeteringanomalydatabasedondistributedsystem AT jiadongli realtimemonitoringandsimulationofmultiuserelectricitymeteringanomalydatabasedondistributedsystem |