Research on anomaly detection and correction of power metering data based on machine learning algorithm
Electric energy measurement is the basis of marketization of electric energy. If the power metering device is abnormal, it will directly affect the economic interests of both sides. At present, the electric energy measurement data of power grid enterprises has generally adopted the mode of remote ce...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | Science and Technology for Energy Transition |
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| Online Access: | https://www.stet-review.org/articles/stet/full_html/2025/01/stet20240325/stet20240325.html |
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| author | Sida Zheng Meiying Zhu Ying Liu |
| author_facet | Sida Zheng Meiying Zhu Ying Liu |
| author_sort | Sida Zheng |
| collection | DOAJ |
| description | Electric energy measurement is the basis of marketization of electric energy. If the power metering device is abnormal, it will directly affect the economic interests of both sides. At present, the electric energy measurement data of power grid enterprises has generally adopted the mode of remote centralized collection. The existing methods of abnormal detection and location of electric energy metering are mainly through the analysis of the abnormal data alarm issued by the electric energy acquisition system and the on-site inspection of the metering device. With the continuous expansion of the scale of electric power data, the existing methods highlight the shortcomings of low accuracy and low efficiency. In order to explore the optimal solution to the above problems, this paper constructs a multi-model fusion anomaly detection method of electric energy measurement data based on machine learning, and gives the anomaly correction scheme of electric energy measurement data. The results show that the fusion model has the best performance in the actual situation, with Area Under Curve (AUC) reaching 0.9653 and True Positive Rate (TPR) exceeding 0.64 under the condition of zero False Positive Threshold (FPT). The comprehensive performance is better than that of other single models. |
| format | Article |
| id | doaj-art-5ad79f0f4bbf4692a4c5f7a1be9c7c18 |
| institution | Kabale University |
| issn | 2804-7699 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | Science and Technology for Energy Transition |
| spelling | doaj-art-5ad79f0f4bbf4692a4c5f7a1be9c7c182025-01-08T11:24:01ZengEDP SciencesScience and Technology for Energy Transition2804-76992025-01-0180610.2516/stet/2024106stet20240325Research on anomaly detection and correction of power metering data based on machine learning algorithmSida Zheng0Meiying Zhu1Ying Liu2State Grid Jibei Electric Power Company Limited Center of MetrologyState Grid Jibei Electric Power Company Limited Center of MetrologyState Grid Jibei Electric Power Company Limited Center of MetrologyElectric energy measurement is the basis of marketization of electric energy. If the power metering device is abnormal, it will directly affect the economic interests of both sides. At present, the electric energy measurement data of power grid enterprises has generally adopted the mode of remote centralized collection. The existing methods of abnormal detection and location of electric energy metering are mainly through the analysis of the abnormal data alarm issued by the electric energy acquisition system and the on-site inspection of the metering device. With the continuous expansion of the scale of electric power data, the existing methods highlight the shortcomings of low accuracy and low efficiency. In order to explore the optimal solution to the above problems, this paper constructs a multi-model fusion anomaly detection method of electric energy measurement data based on machine learning, and gives the anomaly correction scheme of electric energy measurement data. The results show that the fusion model has the best performance in the actual situation, with Area Under Curve (AUC) reaching 0.9653 and True Positive Rate (TPR) exceeding 0.64 under the condition of zero False Positive Threshold (FPT). The comprehensive performance is better than that of other single models.https://www.stet-review.org/articles/stet/full_html/2025/01/stet20240325/stet20240325.htmlmachine learningelectric energy measurement dataanomaly detectioncorrectionmulti-model fusion |
| spellingShingle | Sida Zheng Meiying Zhu Ying Liu Research on anomaly detection and correction of power metering data based on machine learning algorithm Science and Technology for Energy Transition machine learning electric energy measurement data anomaly detection correction multi-model fusion |
| title | Research on anomaly detection and correction of power metering data based on machine learning algorithm |
| title_full | Research on anomaly detection and correction of power metering data based on machine learning algorithm |
| title_fullStr | Research on anomaly detection and correction of power metering data based on machine learning algorithm |
| title_full_unstemmed | Research on anomaly detection and correction of power metering data based on machine learning algorithm |
| title_short | Research on anomaly detection and correction of power metering data based on machine learning algorithm |
| title_sort | research on anomaly detection and correction of power metering data based on machine learning algorithm |
| topic | machine learning electric energy measurement data anomaly detection correction multi-model fusion |
| url | https://www.stet-review.org/articles/stet/full_html/2025/01/stet20240325/stet20240325.html |
| work_keys_str_mv | AT sidazheng researchonanomalydetectionandcorrectionofpowermeteringdatabasedonmachinelearningalgorithm AT meiyingzhu researchonanomalydetectionandcorrectionofpowermeteringdatabasedonmachinelearningalgorithm AT yingliu researchonanomalydetectionandcorrectionofpowermeteringdatabasedonmachinelearningalgorithm |