Big data analytics for identifying electricity theft using machine learning approaches in microgrids for smart communities

Electricity theft (ET) causes major revenue loss in power utilities. It reduces the quality of supply, raises production cost, causes legal consumers to pay the higher cost, and impacts the economy as a whole. In this article, we use the State Grid Corporation of China (SGCC) dataset, which contains...

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Bibliographic Details
Published in:Concurrency and computation 2021-09, Vol.33 (17), p.n/a
Main Authors: Arif, Arooj, Javaid, Nadeem, Aldegheishem, Abdulaziz, Alrajeh, Nabil
Format: Article
Language:English
Subjects:
big data
Classification
Consumers
Datasets
Decision trees
Distributed generation
Electric utilities
Electricity
Electricity consumption
electricity theft detection
Feature extraction
hyperactive optimization toolkit
Interpolation
Machine learning
Oversampling
Production costs
Resampling
smart grids
Theft
urban planning
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Summary:Electricity theft (ET) causes major revenue loss in power utilities. It reduces the quality of supply, raises production cost, causes legal consumers to pay the higher cost, and impacts the economy as a whole. In this article, we use the State Grid Corporation of China (SGCC) dataset, which contains electricity consumption data of 1035 days for two classes: normal and fraudulent. In this work, ET detection model is proposed that consists of four steps: interpolation, data balancing, feature extraction, and classification. First, missing values of the dataset are recovered using the interpolation method. Second, resampling technique is implemented. ET consumers are 9% in the SGCC dataset that make the model inefficient to correctly classify both classes (normal and theft). A hybrid resampling technique is proposed, named synthetic minority oversampling technique with near miss. Third, residual network extracts the latent features from the SGCC dataset. Fourth, three tree based classifiers, such as decision tree (DT), random forest (RF), and adaptive boosting (AdaBoost) are applied to train the encoded feature vectors for classification. Besides, search for good hyperparameters is a challenging task, which is usually done manually and takes a considerable amount of time. To resolve this problem, Bayesian optimizer is used to simplify the tuning process of DT, RF, and AdaBoost. Finally, the results indicate that RF outperforms DT and AdaBoost.
ISSN:1532-0626
1532-0634
DOI:10.1002/cpe.6316