Enhancement of electricity theft detection model for electricity metering system using machine learning and Pearson’s correlation coefficient
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Shuai Yang
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Zhendong Shi
Abstract
Power metering system electricity theft may jeopardize the security and stability of the power system in addition to reducing the financial gains of the power grid. This research suggests a power theft detection model based on the Pearson correlation coefficient and support vector machine (SVM) to increase the detection accuracy. The relationship between the user’s daily electricity consumption and the line loss of electricity of the day in the station region is first examined using the Pearson correlation coefficient to determine whether a user is committing power theft. The SMOTE technique is used to oversample a few classes of samples to address the issue of the limited amount of data on electricity theft users. This prevents sample imbalance from having an impact on the detection model. The SVM algorithm is then used to build the electricity theft detection model, and the Gaussian kernel function is used to translate the linearly indivisible power consumption data to the high-dimensional space to guarantee the dataset’s linear divisibility. With a classification accuracy of 93.8 % and a positive sample classification accuracy of 95.4 % for the detection of electricity theft users, the experimental portion uses the State Grid of China’s actual electricity consumption dataset. Following model training and cross-validation, the results demonstrate that the model can successfully identify electricity theft, demonstrating the model’s superiority and applicability in this area.
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Research ethics: This article does not contain any studies with human participants performed by any of the authors.
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Informed consent: Not applicable.
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Author contributions: Shuai Yang, Qiong Cao is responsible for designing the framework, analyzing the performance, validating the results, and writing the article. Wei Zhang, Zhendong Shi, Yinlong Zhu is responsible for collecting the information required for the framework, provision of software, critical review, and administering the process.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: Authors do not have any conflicts
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Research funding: Authors did not receive any funding.
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Data availability: No datasets were generated or analyzed during the current study.
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- Enhancement of electricity theft detection model for electricity metering system using machine learning and Pearson’s correlation coefficient
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Articles in the same Issue
- Frontmatter
- Research Articles
- Abnormal behaviour recognition technology in smart grid monitoring based on artificial intelligence
- Design of intelligent anti electricity theft monitoring device based on ARM computing modeling
- Translation and context adaptability study of renewable energy technology terms in English and Chinese
- Performance scoring model for new energy vehicles based on Hadoop
- Analysis of electronic product design schemes based on embedded systems
- Use of a Duffing chaotic oscillator with nonlinear stochastic resonance to retrieve faulty power equipment records
- Vocabulary selection methods of Chinese language in solar panel branding and promotion
- Application of distribution network monitoring information automatic verification platform driven by artificial intelligence in improving acceptance testing and power grid operation and maintenance management efficiency
- Data-driven wind power prediction model based on improved generative adversarial network
- Effects evaluation of English listening comprehension in new energy majors with multimedia assistance
- Enhancement of electricity theft detection model for electricity metering system using machine learning and Pearson’s correlation coefficient
- Fusion application of intelligent sensing technology and artificial intelligence algorithm in information verification of new energy grid connection monitoring
- Optimization of high-speed rail energy centralized management in smart grids using dynamic management algorithms
