Electricity theft detection in unbalanced sample distribution: a novel approach including a mechanism of sample augmentation

Yao, Ruizhe; Wang, Ning; Ke, Weipeng; Chen, Peng; Sheng, Xianjun

doi:10.1007/s10489-022-04069-z

Cited by 7 publications

(5 citation statements)

References 29 publications

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“…They are commonly used in regression and classification problems, and are widely employed in ML and data mining. Good decision trees address crucial variables like feature splitting, split values, and stopping splitting [33,34].…”

Section: Decision Treementioning

confidence: 99%

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Exploiting machine learning to tackle peculiar consumption of electricity in power grids: A step towards building green smart cities

Ali,

Khan,

Javaid

et al. 2024

IET Generation Trans & Dist

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The increasing demand for electricity in daily life highlights the need for Smart Cities (SC) to use energy efficiently. Both technical and Non‐Technical Losses (NTL), particularly those resulting from electricity theft, present powerful obstacles; NTL alone can reach billions of dollars. Although Machine Learning (ML) based approaches for NTL detection have been embraced by numerous utilities, there is still a lack of thorough analysis of these methods. Limited research exists on NTL identification evaluation criteria and unbalanced data management in the context of SC. This research compares ML algorithms and data balancing methods to optimize electricity consumption detection. The given research applied the 15 ML techniques of Logistic regression, Bernoulli naive Bayes, Gaussian naive Bayes, K‐Nearest Neighbour, perceptron, passive‐aggressive classifier, quadratic discriminant analysis, SGD classifier, ridge classifier, linear discriminant analysis, decision tree, nearest centroid classifier, multi‐nomial naive Bayes, complement naive Bayes and dummy classifier. While SMOTE, AdaSyn, NRAS, and CCR are considered for data balancing. AUC, F1‐score, and seven relevant performance metrics were used for comparison. We have also implemented SHapely Additive exPlanations (SHAP) for feature importance and model interpretation. Results show varying classifier performance with different balancing methods, emphasizing data preprocessing's role in NTL detection for smart grid security.

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Section: Decision Treementioning

confidence: 99%

“…Algorithm 14 shows the complete procedure of CNB algorithm. Equation (33) shows the formula of complement naive Bayes algorithm rule,…”

Section: Complement Naive Bayesmentioning

confidence: 99%

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Exploiting machine learning to tackle peculiar consumption of electricity in power grids: A step towards building green smart cities

Ali,

Khan,

Javaid

et al. 2024

IET Generation Trans & Dist

View full text Add to dashboard Cite

show abstract

“…Furthermore, the authors in [ 18 , 19 ] adopt a data-driven approach using a Machine Learning technique, XGBoost, without considering any auxiliary information. The study in [ 20 , 21 ] investigates the impact of imbalanced data. The imbalanced data are balanced through synthesized data.…”

Section: Literature Reviewmentioning

confidence: 99%

Electricity Theft Detection in Smart Grids Using a Hybrid BiGRU–BiLSTM Model with Feature Engineering-Based Preprocessing

Munawar

Javaid

Khan

et al. 2022

Sensors

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In this paper, a defused decision boundary which renders misclassification issues due to the presence of cross-pairs is investigated. Cross-pairs retain cumulative attributes of both classes and misguide the classifier due to the defused data samples’ nature. To tackle the problem of the defused data, a Tomek Links technique targets the cross-pair majority class and is removed, which results in an affine-segregated decision boundary. In order to cope with a Theft Case scenario, theft data is ascertained and synthesized randomly by using six theft data variants. Theft data variants are benign class appertaining data samples which are modified and manipulated to synthesize malicious samples. Furthermore, a K-means minority oversampling technique is used to tackle the class imbalance issue. In addition, to enhance the detection of the classifier, abstract features are engineered using a stochastic feature engineering mechanism. Moreover, to carry out affine training of the model, balanced data are inputted in order to mitigate class imbalance issues. An integrated hybrid model consisting of Bi-Directional Gated Recurrent Units and Bi-Directional Long-Term Short-Term Memory classifies the consumers, efficiently. Afterwards, robustness performance of the model is verified using an attack vector which is subjected to intervene in the model’s efficiency and integrity. However, the proposed model performs efficiently on such unseen attack vectors.

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“…According to the survey, various countries have experienced economic losses due to NTLs. For instance, electricity theft in the United States results in an annual economic loss exceeding $6 billion, while the United Kingdom incurs approximately $234 million and China around $560 million [6].…”

mentioning

confidence: 99%

Electricity Theft Detection in Smart Grids Based on Omni-Scale CNN and AutoXGB

Zhu,

Xue,

2024

IEEE Access

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Electricity theft is a prevalent global issue that has detrimental effects on both utility providers and electricity consumers. This phenomenon undermines the economic stability of utility companies, worsens power hazards, and influences electricity costs for consumers. The advancements in Smart Grid technology play an essential role in Electricity Theft Detection (ETD), as they generate large amounts of data that can be effectively utilized for ETD through the application of Machine Learning (ML) and Deep Learning (DL) methodologies.The present study presents a novel approach for ETD by combining Omni-Scale CNN (OS-CNN) and AutoXGB. Firstly, the Piecewise Cubic Hermite Interpolating Polynomial (PCHIP) is employed as the data interpolation technique to address the limitations and missing data in the dataset. Additionally, a combination of the Synthetic Minority Over-Sampling Technique (SMOTE) and the Edited Nearest Neighbors (ENN), known as SMOTEENN, is utilized for data resampling to tackle the issue of class imbalance in the dataset.Secondly, the multi-layer Omni-Scale block stack is employed to effectively cover the receptive fields of diverse time series scales based on a straightforward rule. This enables the Onedimensional Convolutional Neural Network (1D-CNN) to acquire enhanced learning capabilities for both irregular electricity consumption data anomalies and periodic normal electricity consumption patterns in smart grid datasets, facilitating superior extraction of essential data features. The AutoXGB classifier is then utilized to classify the extracted features. AutoXGB possesses the capability of automatically optimizing the hyperparameters required by the model, ensuring that the classification model maintains optimal accuracy and settings. Finally, the method exhibits superior competitiveness compared to other methods on the same dataset. The experimental results demonstrate that the proposed model achieves an accuracy rate of 99.2%, a precision rate of 97.5%, and an area under the ROC curve of 96.7%. These results establish its significant superiority over alternative models.

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Electricity theft detection in unbalanced sample distribution: a novel approach including a mechanism of sample augmentation

Cited by 7 publications

References 29 publications

Exploiting machine learning to tackle peculiar consumption of electricity in power grids: A step towards building green smart cities

Exploiting machine learning to tackle peculiar consumption of electricity in power grids: A step towards building green smart cities

Electricity Theft Detection in Smart Grids Using a Hybrid BiGRU–BiLSTM Model with Feature Engineering-Based Preprocessing

Electricity Theft Detection in Smart Grids Based on Omni-Scale CNN and AutoXGB

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