A Novel Time-Series Transformation and Machine-Learning-Based Method for NTL Fraud Detection in Utility Companies

Badawi, Sufian A.; Guessoum, Djamel; Elbadawi, Isam; Albadawi, Ameera

doi:10.3390/math10111878

Cited by 6 publications

(4 citation statements)

References 37 publications

(57 reference statements)

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“…The classification error is associated with a regression problem and can be found using the cost-sensitive Equation (28):…”

Section: Ridge Classifiermentioning

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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“…The classification error is associated with a regression problem and can be found using the cost-sensitive Equation (28):…”

Section: Ridge Classifiermentioning

confidence: 99%

“…The model achieved 96% accuracy and 0.93 AUC on the SGCC dataset; and 94.5% accuracy and 0.90 AUC for ISET dataset classification. The author in reference [28] proposed a ConvLSTM model for ETD purposes. The pre‐processing steps include data cleaning using KNN imputation and IQR for handling outliers.…”

Section: Literature Reviewmentioning

confidence: 99%

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

“…Key Findings and Implications: A consistent outcome across these studies is the enhanced detection rates and accuracy in identifying electricity theft. The high accuracy, as seen in the Mathematics 2022 study [ 11 ], which achieved a 98% accuracy rate, demonstrates the effectiveness of these modern analytical methods. The implications of these findings are substantial for utility companies, offering more reliable and efficient ways to mitigate electricity theft and manage electricity demand.…”

Section: Introductionmentioning

confidence: 95%

“…The authors in [ 11 ] propose a novel method based on time-series transformation and machine learning for detecting non-technical loss (NTL) fraud in utility companies. The study emphasizes the significance of addressing NTL fraud using advanced machine-learning techniques.…”

Section: Introductionmentioning

confidence: 99%

Hyperparameter Optimization with Genetic Algorithms and XGBoost: A Step Forward in Smart Grid Fraud Detection

Mehdary,

Chehri,

Jakimi

et al. 2024

Sensors

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This study provides a comprehensive analysis of the combination of Genetic Algorithms (GA) and XGBoost, a well-known machine-learning model. The primary emphasis lies in hyperparameter optimization for fraud detection in smart grid applications. The empirical findings demonstrate a noteworthy enhancement in the model’s performance metrics following optimization, particularly emphasizing a substantial increase in accuracy from 0.82 to 0.978. The precision, recall, and AUROC metrics demonstrate a clear improvement, indicating the effectiveness of optimizing the XGBoost model for fraud detection. The findings from our study significantly contribute to the expanding field of smart grid fraud detection. These results emphasize the potential uses of advanced metaheuristic algorithms to optimize complex machine-learning models. This work showcases significant progress in enhancing the accuracy and efficiency of fraud detection systems in smart grids.

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Hybrid non-technical-loss detection in fog-enabled smart grids

Khan,

Jabeen,

Khan

et al. 2024

Sustainable Energy Technologies and Assessments

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A Novel Time-Series Transformation and Machine-Learning-Based Method for NTL Fraud Detection in Utility Companies

Cited by 6 publications

References 37 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

Hyperparameter Optimization with Genetic Algorithms and XGBoost: A Step Forward in Smart Grid Fraud Detection

Hybrid non-technical-loss detection in fog-enabled smart grids

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