Fuzzy Inference Based Electricity Theft Prevention System to Restrict Direct Tapping Over Distribution Line

Jaiswal, Supriya; Ballal, Makarand Sudhakar

doi:10.1007/s42835-020-00408-7

Cited by 11 publications

(2 citation statements)

References 30 publications

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“…Researchers have explored several different methods in the literature to overcome the stated issue, among which the hardware and data-driven approaches are the most widely utilized theft-detection strategies [5]. Hardware-based solutions employ numerous instruments such as balancing or calibration meters, sensors, tamper-evident lock detectors [6], and other devices to detect electric fraud [7]. In these methods, the mentioned devices assist in aggregating the various network characteristic values at different locations so that an immediate discrepancy report can be generated during the occurrence of theft.…”

Section: Introductionmentioning

confidence: 99%

A Novel Feature-Engineered–NGBoost Machine-Learning Framework for Fraud Detection in Electric Power Consumption Data

Hussain

Mustafa

Al-Shqeerat

et al. 2021

Sensors

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This study presents a novel feature-engineered–natural gradient descent ensemble-boosting (NGBoost) machine-learning framework for detecting fraud in power consumption data. The proposed framework was sequentially executed in three stages: data pre-processing, feature engineering, and model evaluation. It utilized the random forest algorithm-based imputation technique initially to impute the missing data entries in the acquired smart meter dataset. In the second phase, the majority weighted minority oversampling technique (MWMOTE) algorithm was used to avoid an unequal distribution of data samples among different classes. The time-series feature-extraction library and whale optimization algorithm were utilized to extract and select the most relevant features from the kWh reading of consumers. Once the most relevant features were acquired, the model training and testing process was initiated by using the NGBoost algorithm to classify the consumers into two distinct categories (“Healthy” and “Theft”). Finally, each input feature’s impact (positive or negative) in predicting the target variable was recognized with the tree SHAP additive-explanations algorithm. The proposed framework achieved an accuracy of 93%, recall of 91%, and precision of 95%, which was greater than all the competing models, and thus validated its efficacy and significance in the studied field of research.

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Section: Introductionmentioning

confidence: 99%

A Novel Feature-Engineered–NGBoost Machine-Learning Framework for Fraud Detection in Electric Power Consumption Data

Hussain

Mustafa

Al-Shqeerat

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Fuzzy inference‐based electricity theft detection and prevention system is described in [15]. However, it neither identifies the sections under theft nor estimates theft currents.…”

Section: Introductionmentioning

confidence: 99%