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

  • Research Article-Computer Engineering and Computer Science
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Abstract

Electricity theft is one of the most significant factors among non-technical losses. Because of electricity theft, genuine users have to pay more, supply quality decreases, and generation load increases. Development in the Internet of Things-based sensors has changed the way to monitor the electricity consumption pattern of the consumers. This electricity consumption data are processed by classification algorithms to identify electricity theft. The electricity consumption data are imbalanced in nature. The objective of this study is to design a machine learning model considering that data imbalance issue is resolved using six data balancing techniques, namely Synthetic Minority over Sampling (SMOTE), Adaptive Synthetic Sampling (ADASYN), Random over Sampler, Support Vector Machine-Synthetic Minority over Sampling, SMOTEENN (Edited Nearest Neighbor) and SMOTE Tomek Links. The designed model consists of two stages. In first stage, twelve classification algorithms (Decision Tree, Adaboost, Extra Tree, Logistic Regression, XGBoost, Light GBM, Multi-Layer Perceptron, Bagging, Random Forest, Support Vector Machine, and Naïve Bayes, K-Nearest Neighbor) are applied on the data balanced by six techniques. In the next stage, two ensemble techniques, namely maximum voting and stacking, are applied to the best five performing algorithms using Python language. Dataset from the State Grid Corporation of China is considered, and algorithms are compared based on accuracy, MCC (Matthews Correlation Coefficient), f1-Score, and log-loss. We observed that SMOTEENN with stacking ensemble algorithm gives the highest performance: accuracy value—97.67%, MCC value—0.9434, log-loss value—1.01, and f1-score value—97.88% among all the experiments performed. The proposed model reported around 3% higher performance than the results presented in the literature. Moreover, the effectiveness of the model is validated by applying the ANOVA (Analysis of variance) one-way statistical test.

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Abbreviations

AdaBoost:

Adaptive Boosting

ANOVA:

Analysis of Variance

ADASYN:

Adaptive Synthetic

ANN:

Artificial Neural Network

AUC:

Area Under Curve

CNN:

Convolutional Neural Network

DANN:

Deep Artificial Neural Network

DT:

Decision Tree

EMD:

Empirical Mode Decomposition

ET:

Extra Tree

ETD:

Electricity Theft Detection

FA:

Firefly Algorithm

FIS:

Fuzzy Inference System

GA:

Genetic Algorithm

KNN:

K-Nearest Neighbor

LightGBM:

Light Gradient Boosting Machine

LR:

Logistic Regression

LSTM:

Long Short-Term Memory

MAP:

Mean Average Precision

MIN–MAX:

Minimum–Maximum

MCC:

Matthews Correlation Coefficient

ML:

Machine Learning

MLP:

Multi-Layer Perceptron

NB:

Naïve Bayes

OPF:

Optimum Path Forest

PSO:

Particle Swarm Optimization

RF:

Random Forest

ROC:

Receiver Operating Characteristic

RUSBoost:

Random Undersampling Boosting

SGCC:

State Grid Corporation of China

SVM:

Support Vector Machine

SMOTE:

Synthetic Minority Over Sampling

SMOTEENN:

Synthetic Minority Over Sampling Edited Nearest Neighbor

SVM-SMOTE:

SVM- Synthetic Minority Over Sampling

VGG:

Visual Geomtry Group

XGBoost:

EXtreme Gradient Boosting

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  1. IIT, Roorkee, India

    Alisha Banga, Ravinder Ahuja & S. C. Sharma

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  1. Alisha Banga
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Banga, A., Ahuja, R. & Sharma, S.C. Accurate Detection of Electricity Theft Using Classification Algorithms and Internet of Things in Smart Grid. Arab J Sci Eng 47, 9583–9599 (2022). https://doi.org/10.1007/s13369-021-06313-z

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