Theft Detection Research Articles

Electric theft detection in advanced metering infrastructure using Jaya optimized combined Kernel‐Tree boosting classifier—A novel sequentially executed supervised machine learning approach

This paper presents a novel, sequentially executed supervised machine learning-based electric theft detection framework using a Jaya-optimized combined Kernel and Tree Boosting (KTBoost) classifier. It utilizes the intelligence of the XGBoost algorithm to estimate the missing values in the acquired dataset during the data pre-processing phase. An oversampling algorithm based on the Robust-SMOTE technique is utilized to avoid the unbalanced data class distribution issue. Afterward, with the aid of few very significant statistical, temporal, and spectral features extracted from the acquired kWh dataset, the complex underlying data patterns are comprehended to enhance the accuracy and detection rate of the classifier. For effectively classifying the consumers into “Honest” and “Fraudster,” the ensemble machine learning-based classifier KTBoost, with Jaya algorithm optimized hyperparameters, is utilized. Finally, the developed model is re-trained using a reduced set of highly important features to minimize the computational resources without compromising the performance of the developed model. The outcome of this study reveals that the proposed theft detection method achieves the highest accuracy (93.38%), precision (95%), and recall (93.18%) among all the studied methods, thus signifying its importance in the studied area of research.

Evaluation of Online Machine Learning Algorithms for Electricity Theft Detection in Smart Grids

Electricity theft-induced power loss is a pressing issue in both traditional and smart grid environments. In smart grids, smart meters can be used to track power consumption behaviour and detect any suspicious activity. However, smart meter readings can be compromised by deploying intrusion tactics or launching cyber attacks. In this regard, machine learning models can be used to assess the daily consumption patterns of customers and detect potential electricity theft incidents. Whilst existing research efforts have extensively focused on batch learning algorithms, this paper investigates the use of online machine learning algorithms for electricity theft detection in smart grid environments, based on a recently proposed dataset. Several algorithms including Naive Bayes, K-nearest Neighbours, K-nearest Neighbours with self-adjusting memory, Hoeffding Tree, Extremely Fast Decision Tree, Adaptive Random Forest and Leveraging Bagging are considered. These algorithms are evaluated using an online machine learning platform considering both binary and multi-class theft detection scenarios. Evaluation metrics include prediction accuracy, precision, recall, F-1 score and kappa statistic. Evaluation results demonstrate the ability of the Leveraging Bagging algorithm with an Adaptive Random Forest base classifier to surpass all other algorithms in terms of all the considered metrics, for both binary and multi-class theft detection. Hence, it can be considered as a viable option for electricity theft detection in smart grid environments.

Electricity Theft Detection and Localization in Smart Grids for Industry 4.0

Industry 4.0 is considered as the fourth revolution in industrial sector that represents the digitization of production process in a smarter way. Industry 4.0 refers to the intelligent networking of machines, their processes, and infrastructure, as well as the use of information and computer technology to transform industry. The technologies like industrial internet of things (IIoT), big data analytics, cloud computing, augmented reality and cyber security are the main pillars of industry 4.0. Industry 4.0, in particular, is strongly reliant on the IIoT that refers to the application of internet of things (IoT) in industrial sector like smart grids (SG). IoT can be deployed in different sections of smart grids that include electricity generation, transmission, distribution and its utilization at customer side. However, the key obstacles that deployment faces are theft detection and localization in smart grids. Electricity theft causes huge losses to the electricity providers and in turn contributes in increasing the fiscal deficit of the government. Therefore, this paper provides an IoT based solution that implements a system which automatically checks for electricity theft in between the regional transformer and customer side. The system also sends the latitude and longitude coordinates of the place where the theft has been detected on to the thingspeak cloud platform. Further, the exact location of the theft has been displayed on the webpage using geolocation application programing interface (API) on Google maps. The proposed system can be easily integrated in the smart grid network structure and helps companies to increase their business.

Electricity Theft Detection in Smart Grids Based on Deep Neural Network

Electricity theft is a global problem that negatively affects both utility companies and electricity users. It destabilizes the economic development of utility companies, causes electric hazards and impacts the high cost of energy for users. The development of smart grids plays an important role in electricity theft detection since they generate massive data that includes customer consumption data which, through machine learning and deep learning techniques, can be utilized to detect electricity theft. This paper introduces the theft detection method which uses comprehensive features in time and frequency domains in a deep neural network-based classification approach. We address dataset weaknesses such as missing data and class imbalance problems through data interpolation and synthetic data generation processes. We analyze and compare the contribution of features from both time and frequency domains, run experiments in combined and reduced feature space using principal component analysis and finally incorporate minimum redundancy maximum relevance scheme for validating the most important features. We improve the electricity theft detection performance by optimizing hyperparameters using a Bayesian optimizer and we employ an adaptive moment estimation optimizer to carry out experiments using different values of key parameters to determine the optimal settings that achieve the best accuracy. Lastly, we show the competitiveness of our method in comparison with other methods evaluated on the same dataset. On validation, we obtained 97% area under the curve (AUC), which is 1% higher than the best AUC in existing works, and 91.8% accuracy, which is the second-best on the benchmark.

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

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.

Augmenting the detection of online image theft using diagonal transformation and iris biometric watermarking

Augmenting the detection of online image theft using diagonal transformation and iris biometric watermarking

AlexNet, AdaBoost and Artificial Bee Colony Based Hybrid Model for Electricity Theft Detection in Smart Grids

Electricity theft (ET) is an utmost problem for power utilities because it threatens public safety, disturbs the normal working of grid infrastructure and increases revenue losses. In the literature, many machine learning (ML), deep learning (DL) and statistical based models are introduced to detect ET. However, these models do not give optimal results due to the following reasons: curse of dimensionality, class imbalance problem, inappropriate hyper-parameter tuning of ML and DL models, etc. Keeping the aforementioned concerns in view, we introduce a hybrid DL model for the efficient detection of electricity thieves in smart grids. AlexNet is utilized to handle the curse of dimensionality issue while the final classification of energy thieves and normal consumers is performed through adaptive boosting (AdaBoost). Moreover, class imbalance problem is resolved using an undersampling technique, named as near miss. Furthermore, hyper-parameters of AdaBoost and AlexNet are tuned using artificial bee colony optimization algorithm. The real smart meters’ dataset is used to assess the efficacy of the hybrid model. The substantial amount of simulations proves that the hybrid model obtains the highest classification results as compared to its counterparts. Our proposed model obtains 88%, 86%, 84%, 85%, 78% and 91% accuracy, precision, recall, F1-score, Matthew correlation coefficient and area under the curve receiver operating characteristics, respectively.

An IoT based Anti Theft Detection and Notification System for Two Wheelers

An IoT based Anti Theft Detection and Notification System for Two Wheelers

A Hybrid ConvLSTM-Based Anomaly Detection Approach for Combating Energy Theft

In a conventional power grid, energy theft is difficult to detect due to limited communication and data transition. The smart meter along with big data mining technology leads to significant technological innovation in the field of energy theft detection. This paper proposes a convolutional long short-term memory (ConvLSTM) based energy theft detection (ETD) model to identify electricity theft users. In this work, electricity consumption data is reshaped quarterly into a two-dimensional matrix and used as the sequential input to the ConvLSTM. The convolutional neural network (CNN) embedded into the long short-term memory (LSTM) can better learn the features of the data on different quarters, months, weeks, and days. Besides, the proposed model incorporates batch normalization. This technique allows the proposed ETD model to support raw format electricity consumption data input, reducing training time and increasing the efficiency of model deployment. The result of the case study shows that the proposed ConvLSTM model exhibits good robustness. It outperforms the multilayer perceptron (MLP) and CNN-LSTM in terms of performance metrics and model generalization capability. Moreover, the result also demonstrates that K-fold cross-validation can improve the ETD prediction accuracy.

Non-Technical Loss Detection Using Deep Reinforcement Learning for Feature Cost Efficiency and Imbalanced Dataset

One of the problems of the electricity grid system is electricity loss due to energy theft, which is known as non-technical loss (NTL). The sustainability and stability of the grid system are threatened by the unexpected electricity losses. Energy theft detection based on data analysis is one of the solutions to alleviate the drawbacks of NTL. The main problem of data-based NTL detection is that collected electricity usage dataset is imbalanced. In this paper, we approach the NTL detection problem using deep reinforcement learning (DRL) to solve the data imbalanced problem of NTL. The advantage of the proposed method is that the classification method is adopted to use the partial input features without pre-processing method for input feature selection. Moreover, extra pre-processing steps to balance the dataset are unnecessary to detect NTL compared to the conventional NTL detection algorithms. From the simulation results, the proposed method provides better performances compared to the conventional algorithms under various simulation environments.

A Novel Unsupervised Data-Driven Method for Electricity Theft Detection in AMI Using Observer Meters

The smart meter data of the Advanced Metering Infrastructure (AMI) can be tampered by electricity thieves with advanced digital instruments or cyber attacks to reduce their electricity bills, which causes devastating financial losses to utilities. A novel unsupervised data-driven method for electricity theft detection in AMI is proposed in this paper. The method incorporates observer meter data, wavelet-based feature extraction, and fuzzy c-means (FCM) clustering. A new anomaly score is developed based on the degree of cluster membership information produced by FCM clustering to differentiate normal and fraudulent users.We perform an ablation study to investigate the impact of key components of the proposed method on the performance by using a publicly available smart meter dataset. The results show that all key components of the proposed method contribute significantly to the performance improvement. The proposed method is compared with a set of baselines including state-of-the-art methods by using smart meter data of both business users and residential users. The comparison results indicate that the proposed method achieves significantly better detection performance than all baseline methods. We also show that the proposed method maintains a good performance when the detection time frame is reduced from 30 days to 20 days.

Electricity Theft Detection Using Euclidean and Graph Convolutional Neural Networks

The widespread penetration of advanced metering infrastructure brings an opportunity to detect electricity theft by analyzing the electricity consumption data collected from smart meters. However, existing models have poor performance in electricity theft detection, since most of them fail to capture the time dependence, periodicity, and latent feature from complex electricity consumption data. To address above concerns, a graph convolutional neural network (GCN) and a Euclidean convolutional neural network (CNN) are combined to form a novel model for electricity theft detection in this paper. On one hand, the high-dimensional power load curves are modeled as a graph from a new perspective on graph theory. Then, the GCN depicts the time dependence and periodicity by performing graph convolutional operations. On the other hand, the CNN captures the latent features from the power load curves by carrying out Euclidean convolutional procedures. Numerical simulations show that the proposed model integrates the benefits of GCN and CNN, leading to superiority over the popular benchmarks in electricity theft detection.

Electricity Theft Detection using Machine Learning

This research work dealt with the indiscriminate theft of electric power, reported as a non-technical loss, affecting electric distribution companies and customers, triggering serious consequences including fires and blackouts. The research focused on recommending the best prediction model using Machine Learning in electrical energy theft. The source of the information on the electricity consumption of 42372 consumers was a dataset published in the State Grid Corporation of China. The method used was data imputation, data balancing (oversampling and under sampling), and feature extraction to improve energy theft detection. Five Machine Learning models were tested. As a result, the accuracy indicator of the SVM model was 81%, K-Nearest Neighbors 79%, Random Forest 80%, Logistic Regression 69%, and Naive Bayes 68%. It is concluded that the best performance, with an accuracy of 81%, is obtained by using the SVM model.

Performance Analysis of Electricity Theft Detection for the Smart Grid: An Overview

Electricity theft has been a growing concern for the smart grid. It can be defined as follows: illegal customers use energy from electric utilities without a contract or manipulate their meter readings to pay less or not pay the electricity bill. Over the past decade, significant studies have been done to prevent and combat theft. This article aims to provide a general overview of the progress of electricity theft detection, including threat models, datasets and input features adopted, methodologies and techniques, and evaluation metrics. We also make a performance comparison for each detection method. Finally, the challenges and future research directions are presented in this article.

Design and Implementation of a Cloud-Based Load Monitoring Scheme for Electricity Theft Detection on a Conventional Grid

Electricity theft is one of the problems - encountered by the utilities - that leads to losses of revenue. Manual monitoring of the consumers’ activities which shows the energy data consumed on the conventional grid has largely contributed to electricity theft on the grid. Significantly, the energy meter deployed to monitor the load cannot store and transmit energy data in real-time. This has made electricity theft on the grid unnoticed. This paper presents the development of a monitoring scheme for an electronic meter on the conventional grid with the capability to monitor, store and transmit consumers’ energy data to the cloud. It consists of two units: the indoor and the outdoor unit. Energy data is transferred wirelessly between the units via the Wi-Fi modules. The outdoor unit compares the data and transfers the outcome to the ThinkSpeak cloud server. The transferred energy data can be accessed in real-time from the cloud or downloaded in comma-separated values format for further use. In order to verify the functionality of the proposed scheme, two scenarios of electricity theft - partial bypassing and full bypassing - are carried out. The obtained results show that the scheme can detect the theft and log the data to the cloud successfully.

Prototype of Energy Consumption Monitoring and Energy Theft Detection Based on IoT

Energy management in home is gaining more attention as people try to use sustainable energy sources. This attempt to improve the use of infrastructure for energy production and distribution is expected to include smart grid systems. The incorporation of Internet of Things (IoT)-enabled smart devices into an ecosystem designed for maximum energy efficiency has been proposed for Smart Home Energy Management Systems (SHEMS). Smart homes are expensive, but smart plugs can change normal home devices relatively smart. This paper proposes a prototype design for a SHEMS it monitors and controls the energy consumption of smart devices to reduce the electricity bill and also detect energy theft.

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

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.

Improving Electricity Theft Detection using Combination of Improved Crow Search Algorithm and Support Vector Machine

Energy losses in the electricity distribution and transmission network and electricity theft detection are major challenges of electricity suppliers around the world. Advanced metering infrastructure (AMI) is an essential segment of the smart grids that is responsible for gathering, measuring and analyzing consuming data of customers. The addition of a security layer to AMI has paved the way for the electricity theft in new ways. The analysis of consumed data related to users is one of the essential resources to identify electricity thieves. In this paper, the crow search algorithm (CSA) is improved and the factors of weight (w ) and awareness probability (AP ) are obtained dynamically and used to adjust the parameters c and γ of support vector machine (SVM). The results illustrate that the ICSA-SVM framework has acceptable performance and detects fraudulent customers with a high accuracy.

Identification of Electricity Threat and Performance Analysis using LSTM and RUSBoost Methodology

In power systems, electrical losses can be categorized into two types, namely, Technical Losses (TLs) and Non-Technical Losses (NTLs). It has been identified that NTL is more hazardous when compared to TL, primarily due to the factors such as billing errors, faulty meters, electricity theft etc. This proves to be crucial in the power system and will result in heavy financial loss for the utility companies. To identify theft, both academia and industry, use a mechanism known as Electricity Theft Detection (ETD). However, ETD is not used efficiently because of handling high-dimensional data, overfitting issues and imbalanced data. Hence, in this paper, a means of addressing this issue using Random Under-Sampling Boosting (RUSBoost) technique and Long Short-Term Memory (LSTM) technique is proposed. Here, parameter optimization is performed using RUSBoost and abnormal electricity patterns are detected by LSTM technique. Electricity data are pre-processed in the proposed methodology, using interpolation and normalization methods. The data thus obtained are then sent to the LSTM module where feature extraction takes place. These features are then classified using RUSBoost algorithm. Based on the output simulated, it is identified that this methodology addresses several issues such as handling and overfitting of massive time series data and data imbalancing. Moreover, this technique also proves to be more efficient than several other methodologies such as Logistic Regression (LR), Convolutional Neural Network (CNN) and Support Vector Machine (SVM). A comparison is also drawn, taking into consideration the parameters such as Receiver operating characteristics, recall, precision and F1-score.

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

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