Accurate Detection of Electricity Theft Using Classification Algorithms and Internet of Things in Smart Grid

Banga, Alisha; Ahuja, Ravinder; Sharma, S. C.

doi:10.1007/s13369-021-06313-z

Cited by 10 publications

(12 citation statements)

References 59 publications

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“…The proposed BiLSTM-LogitBoost stacking ensemble model, proposed for ETD in SGs, is evaluated and discussed in this section. Some recent benchmarks, such as SVM [19], [71], logistic regression (LR) [37], decision tree (DT) [37], LSTM [21], [71], adaptive boosting (AdaBoost) [37], BiLSTM [64], LogitBoost [65], and LSTM-AdaBoost [72] are also implemented for ETD and their results are compared with the proposed model. LogitBoost with n_estimators = 25 is employed as a benchmark technique to our proposed model.…”

Section: Discussion Of the Simulation Resultsmentioning

confidence: 99%

“…As previous both categories of ETD techniques are highlyexpensive, therefore, many researchers have moved towards the third category that is data mining based ETD techniques [11], [20], [21], [22], [23], [32], [33], [34], [35], [36], [37], and [38], to handle the problem of electricity theft in SGs. Authors in [11] propose a deep model for electricity theft and non-theft consumers' classification in SGs.…”

Section: Related Workmentioning

confidence: 99%

“…The EC samples from residential energy disaggregation dataset (REDD) are employed for theft analysis in SGs. Furthermore, in [37], fourteen classification techniques (including stacking and maximum voting ensembles) are employed with six data balancing (DB) techniques to finalize the best combination that detects NTLs in SGs. Finally, a combined synthetic minority oversampling technique and edited nearest neighbor (SMOTE-ENN) technique in combination with the stacking classifier is considered to be the best performer for ETD in SGs.…”

Section: Related Workmentioning

confidence: 99%

“…Such situations negatively affect the classifiers and lead it to the overfitting issue. In the existing literature, the imbalance between data classes is tackled by some researchers using RUS [23], AdaSyn [37], and SMOTE [37], [62] techniques. However, RUS is prone to underfitting problem.…”

Section: Data Balancingmentioning

confidence: 99%

“…Hence, the proposed model's performance evaluation is done via different performance metrics. The mathematical formulas of these metrics are provided in Equations 3-6 [37], [74].…”

Section: B Performance Measuresmentioning

confidence: 99%

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RFE Based Feature Selection and KNNOR Based Data Balancing for Electricity Theft Detection Using BiLSTM-LogitBoost Stacking Ensemble Model

et al. 2022

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Obtaining outstanding electricity theft detection (ETD) performance in the realm of advanced metering infrastructure (AMI) and smart grids (SGs) is quite difficult due to various issues. The issues include limited availability of theft data as compared to benign data, neglecting dimensionality reduction, usage of the standalone (single) electricity theft detectors, etc. These issues lead the classification techniques to low accuracy, minimum precision, low F1 score, and overfitting problems. For these reasons, it is extremely crucial to design such a novel strategy that is capable to tackle these issues and yield outstanding ETD performance. In this article, electricity theft happening in SGs is detected using a novel ETD approach. The proposed approach comprises recursive feature elimination (RFE), k nearest neighbor oversampling (KNNOR), bidirectional long short term memory (BiLSTM), and logit boosting (LogitBoost) techniques. Furthermore, three BiLSTM networks and a LogitBoost model are combined to make a BiLSTM-LogitBoost stacking ensemble model. Data preprocessing and feature selection followed by data balancing and electricity theft classification are the four major stages of the model proposed for ETD. It is obvious from the simulations performed using state grid corporation of China (SGCC)'s electricity consumption (EC) data that our proposed model achieves 96.32% precision, 94.33% F1 score, and 89.45% accuracy, which are higher than all the benchmarks employed in this study.

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Section: Discussion Of the Simulation Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Data Balancingmentioning

confidence: 99%

“…Hence, the proposed model's performance evaluation is done via different performance metrics. The mathematical formulas of these metrics are provided in Equations 3-6 [37], [74].…”

Section: B Performance Measuresmentioning

confidence: 99%

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