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

Pamir,; Javaid, Nadeem; Almogren, Ahmad; Adil, Muhammad; Javed, Muhammad Umar

doi:10.1109/access.2022.3215532

Cited by 12 publications

(9 citation statements)

References 67 publications

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“…To counter this, various methods are used to balance the data, ensuring that the models can accurately detect instances of theft: Oversampling the Minority Class: This method increases the number of theft cases in the dataset by either duplicating existing cases or creating new, synthetic examples. A popular technique for generating these synthetic examples is SMOTE (Synthetic Minority Over-sampling Technique), which creates new instances by blending between actual examples [ 33 ]. Undersampling the Majority Class: Conversely, this approach reduces the number of legitimate usage instances to equal the theft cases, though it may result in the loss of some valuable data [ 34 ].…”

Section: Sgcc Dataset and Explorationmentioning

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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Section: Sgcc Dataset and Explorationmentioning

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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“…Accuracy, precision, area under the receiver operating characteristic curve (AUC) score, F1 score, and recall are the performance metrics utilized in this paper to assess the performance of the proposed model. 19 These measures are the ones that are most frequently used in the ETD literature. [46][47][48][49] To calculate the aforementioned metrics, we need to calculate the confusion matrix.…”

Section: Performance Evaluation Measuresmentioning

confidence: 99%

“…When tuning of hyperparameters is done manually instead of automated, it affects the ML algorithm's performance in terms of computational overhead, precision, F1 score, and accuracy. 19 In this study, we develop a novel electricity theft detector called SSA-GCAE-CSLSTM that makes use of users' EC history to address issues with existing electricity theft classification techniques. This detector is based on gated recurrent unit (GRU), convolutional autoencoder (CAE), salp swarm algorithm (SSA) optimization, and cost-sensitive learning (class-weighting) and long short-term memory (CSLSTM).…”

Section: Introductionmentioning

confidence: 99%

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Electricity theft detection for energy optimization using deep learning models

Pamir,

Javaid,

Javed

et al. 2023

Energy Science & Engineering

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The rapid increase in nontechnical loss (NTL) has become a principal concern for distribution system operators (DSOs) over the years. Electricity theft makes up a major part of NTL. It causes losses for the DSOs and also deteriorates the quality of electricity. The introduction of advanced metering infrastructure along with the upgradation of the traditional grids to the smart grids (SGs) has helped the electric utilities to collect the electricity consumption (EC) readings of consumers, which further empowers the machine learning (ML) algorithms to be exploited for efficient electricity theft detection (ETD). However, there are still some shortcomings, such as class imbalance, curse of dimensionality, and bypassing the automated tuning of hyperparameters in the existing ML‐based theft classification schemes that limit their performances. Therefore, it is essential to develop a novel approach to deal with these problems and efficiently detect electricity theft in SGs. Using the salp swarm algorithm (SSA), gate convolutional autoencoder (GCAE), and cost‐sensitive learning and long short‐term memory (CSLSTM), an effective ETD model named SSA–GCAE–CSLSTM is proposed in this work. Furthermore, a hybrid GCAE model is developed via the combination of gated recurrent unit and convolutional autoencoder. The proposed model comprises five submodules: (1) data preparation, (2) data balancing, (3) dimensionality reduction, (4) hyperparameters' optimization, and (5) electricity theft classification. The real‐time EC data provided by the state grid corporation of China are used for performance evaluations via extensive simulations. The proposed model is compared with two basic models, CSLSTM and GCAE–CSLSTM, along with seven benchmarks, support vector machine, decision tree, extra trees, random forest, adaptive boosting, extreme gradient boosting, and convolutional neural network. The results exhibit that SSA–GCAE–CSLSTM yields 99.45% precision, 95.93% F1 score, 92.25% accuracy, and 71.13% area under the receiver operating characteristic curve score, and surpasses the other models in terms of ETD.

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“…Among these, electricity theft is responsible for a remarkable amount of loss [2]. The illegal usage of energy in multiple ways is referred to as stealing electricity, also known as electricity theft [3]. The principal reason of energy theft is tapping, which is responsible for almost 80% of the total NTLs [1].…”

Section: Introductionmentioning

confidence: 99%

Employing a Machine Learning Boosting Classifiers Based Stacking Ensemble Model for Detecting Non Technical Losses in Smart Grids

et al. 2022

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In the modern world, there are numerous opportunities that help in the detection of electricity theft happening in the realm of electricity grids due to the widespread shifting of people from old metering infrastructure to advanced metering infrastructure (AMI). It is done by studying the consumers' energy consumption (EC) readings provided by the smart meters (SM). The literature introduces a variety of machine learning (ML) and deep learning (DL) strategies to use EC data for identifying power theft in smart grids (SGs). However, the existing schemes provide low performance in terms of electricity theft detection (ETD) due to the usage of imbalanced data and using schemes in an individual manner. Moreover, the existing detectors are validated using a limited number of performance evaluation measures, which are not suitable for conducting model's comprehensive validation. To tackle the above mentioned problems, an ML boosting classifiers based stacking ensemble model (MLBCSM) is proposed followed by adaptive synthetic sampling technique (ADASYN) in the underlying work. Data preprocessing followed by data balancing and classification are the three major parts of the model introduced in this work. Besides, the EC data acquired from the consumers' SMs is used for detecting electricity theft. Moreover, the simulation results reveal that MLBCSM combines the benefits of adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), histogram boosting (HistBoost), categorical boosting (CatBoost), and light gradient boosting (LGBoost). Additionally, the model's validation is ensured via different metrics. It is deduced via extensive simulations that the proposed model's outcomes are superior to those produced by the individual models in terms of ETD.

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

Cited by 12 publications

References 67 publications

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

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

Electricity theft detection for energy optimization using deep learning models

Employing a Machine Learning Boosting Classifiers Based Stacking Ensemble Model for Detecting Non Technical Losses in Smart Grids

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