Conference Paper

Electricity Theft Detection using Pipeline in Machine Learning

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... This work is an extension of [35]. The main contributions of this work are listed below. ...
... So, the ratio is 1:10, which proves that the data set is highly imbalanced. The dataset cannot be used without preprocessing as imbalanced dataset negatively affects the performance of a classifier [35]. ...
... • Support Vector Machine: it is famous for both regression and classification problems as it is a flexible and a powerful supervised algorithm [59], [35]. Table 6 shows the values of parameters used for simulations of SVM. ...
Article
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In smart grids, electricity theft is the most significant challenge. It cannot be identified easily since existing methods are dependent on specific devices. Also, the methods lack in extracting meaningful information from high-dimensional electricity consumption data and increase the false positive rate that limit their performance. Moreover, imbalanced data is a hurdle in accurate electricity theft detection (ETD) using data driven methods. To address this problem, sampling techniques are used in the literature. However, the traditional sampling techniques generate insufficient and unrealistic data that degrade the ETD rate. In this work, two novel ETD models are developed. A hybrid sampling approach, i.e., synthetic minority oversampling technique with edited nearest neighbor, is introduced in the first model. Furthermore, AlexNet is used for dimensionality reduction and extracting useful information from electricity consumption data. Finally, a light gradient boosting model is used for classification purpose. In the second model, conditional wasserstein generative adversarial network with gradient penalty is used to capture the real distribution of the electricity consumption data. It is constructed by adding auxiliary provisional information to generate more realistic data for the minority class. Moreover, GoogLeNet architecture is employed to reduce the dataset’s dimensionality. Finally, adaptive boosting is used for classification of honest and suspicious consumers. Both models are trained and tested using real power consumption data provided by state grid corporation of China. The proposed models’ performance is evaluated using different performance metrics like precision, recall, accuracy, F1-score, etc. The simulation results prove that the proposed models outperform the existing techniques, such as support vector machine, extreme gradient boosting, convolution neural network, etc., in terms of efficient ETD.
... Support Vector Machine (SVM) is the most commonly used technique for electricity theft detection to achieve a high detection rate and fewer false alarms. Certain aspects of the electricity consumption data such as historical consumption data (location, seasonality, and category), load profile information, identification of consumers with a high probability of abnormal behaviour, and high dimensional data have been explored well using SVMs [9], Genetic algorithm-based SVM [5], fuzzy-based SVMs [10], and PCA based SVMs [11]. Electricity thieves have also been identified by analyzing their load profiles at different hierarchies of the power grid (transmission, distribution, and consumer) using hybrid SVM models such as decision tree-based SVMs [12], decision trees-k-nearest neighbour SVMs [13], Extreme learning machine (ELM), online sequential ELMs [14], and even multi-class SVMs [15]. ...
... Ignoring such missing values might lead to downsizing the dataset, which poses a significant challenge in carrying out reliable analysis. Previous works [11], [23]- [25] have used linear interpolation, mean of previous and following day consumption's, filling with mean or median of a complete column, and dropping rows which have missing values beyond a certain threshold. Such methods perform well for isolated RQ1: How to handle large gaps, i.e., consecutive missing values, in time series data with high seasonal trends in an effective way? ...
Conference Paper
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Artificial intelligence-based techniques applied to the electricity consumption data generated from the smart grid prove to be an effective solution in reducing Non Technical Loses (NTLs), thereby ensures safety, reliability, and security of the smart energy systems. However, imbalanced data, consecutive missing values, large training times, and complex architectures hinder the real time application of electricity theft detection models. In this paper, we present EnsembleNTLDetect, a robust and scalable electricity theft detection framework that employs a set of efficient data pre-processing techniques and machine learning models to accurately detect electricity theft by analysing consumers' electricity consumption patterns. This framework utilises an enhanced Dynamic Time Warping Based Imputation (eDTWBI) algorithm to impute missing values in the time series data and leverages the Near-miss undersampling technique to generate balanced data. Further, stacked autoencoder is introduced for dimensionality reduction and to improve training efficiency. A Conditional Generative Adversarial Network (CTGAN) is used to augment the dataset to ensure robust training and a soft voting ensemble classifier is designed to detect the consumers with aberrant consumption patterns. Furthermore, experiments were conducted on the real-time electricity consumption data provided by the State Grid Corporation of China (SGCC) to validate the reliability and efficiency of EnsembleNTLDetect over the state-of-the-art electricity theft detection models in terms of various quality metrics.
... Support Vector Machine (SVM) is the most commonly used technique for electricity theft detection to achieve a high detection rate and fewer false alarms. Certain aspects of the electricity consumption data such as historical consumption data (location, seasonality, and category), load profile information, identification of consumers with a high probability of abnormal behaviour, and high dimensional data have been explored well using SVMs [9], Genetic algorithm-based SVM [5], fuzzy-based SVMs [10], and PCA based SVMs [11]. Electricity thieves have also been identified by analyzing their load profiles at different hierarchies of the power grid (transmission, distribution, and consumer) using hybrid SVM models such as decision tree-based SVMs [12], decision trees-k-nearest neighbour SVMs [13], Extreme learning machine (ELM), online sequential ELMs [14], and even multi-class SVMs [15]. ...
... Ignoring such missing values might lead to downsizing the dataset, which poses a significant challenge in carrying out reliable analysis. Previous works [11], [23]- [25] have used linear interpolation, mean of previous and following day consumption's, filling with mean or median of a complete column, and dropping rows which have missing values beyond a certain threshold. Such methods perform well for isolated RQ1: How to handle large gaps, i.e., consecutive missing values, in time series data with high seasonal trends in an effective way? ...
Preprint
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Artificial intelligence-based techniques applied to the electricity consumption data generated from the smart grid prove to be an effective solution in reducing Non Technical Loses (NTLs), thereby ensures safety, reliability, and security of the smart energy systems. However, imbalanced data, consecutive missing values, large training times, and complex architectures hinder the real time application of electricity theft detection models. In this paper, we present EnsembleNTLDetect, a robust and scalable electricity theft detection framework that employs a set of efficient data pre-processing techniques and machine learning models to accurately detect electricity theft by analysing consumers' electricity consumption patterns. This framework utilises an enhanced Dynamic Time Warping Based Imputation (eDTWBI) algorithm to impute missing values in the time series data and leverages the Near-miss undersampling technique to generate balanced data. Further, stacked autoencoder is introduced for dimensionality reduction and to improve training efficiency. A Conditional Generative Adversarial Network (CTGAN) is used to augment the dataset to ensure robust training and a soft voting ensemble classifier is designed to detect the consumers with aberrant consumption patterns. Furthermore, experiments were conducted on the real-time electricity consumption data provided by the State Grid Corporation of China (SGCC) to validate the reliability and efficiency of EnsembleNTLDetect over the state-of-the-art electricity theft detection models in terms of various quality metrics.
... In the last few decades, electrical power has become a backbone for the development of any country [112,113,114]. It has the potential to either raise or reduce the country's economy. ...
... However, the existing methods for detecting this criminal electrical theft behavior are diversified and complicated due to the unbalanced nature of the data sets. The work of [17] addresses these difficulties by developing a model, integrating 3 algorithms, first applying the synthetic minority oversampling technique (SMOTE) to balance the data set, secondly, the integration of the function of the kernel and principal component analysis (KPCA) for feature extraction from high-dimensional time-series data, and Support Vector Machine (SVM) for classification. ...
Thesis
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Instead of planting new electricity generation units, there is a need to design an efficient energy management system to achieve a normalized trend of power consumption. Smart grid has been evolved as a solution, where Demand Response (DR) strategy is used to modify the consumer's nature of demand. In return, utilities pay incentives to the consumer. This concept is equally applicable on residential and commercial areas; however, the increasing load demand and irregular electricity load profile in residential area have encouraged us to propose an efficient home energy management system for optimal scheduling of home appliances. Whereas, electricity consumers have stochastic nature, for which nature-inspired optimization techniques provide optimal solutions. However, these optimization techniques behave stochastically according to the situation. For this reason, we have proposed different optimization techniques for different scenarios. The objectives of this thesis include: reduction in electricity bill and peak to average ratio, minimization of waiting time to start appliances (comfort maximization) and minimization of wastage of surplus energy by exploiting the coordination among appliances and homes. In order to meet the electricity demand of the consumers, the energy consumption patterns of a consumer are maintained through scheduling the appliances in day-ahead and realtime bases. It is applicable by the defined fitness criterion for the proposed hybrid bacterial foraging genetic algorithm and hybrid elephant adaptive cuckoo search optimization techniques, which helps in balancing the load during On-peak and Off-peak hours. Moreover, the concept of coordination and coalition among home appliances is presented for real-time scheduling. The fitness criterion helps the scheduler to optimally decide the ON/OFF status of appliances in order to reduce the waiting time of the appliance. A multi-objective optimization based solution is proposed to resolve the trade-off between conflicting objectives: electricity bill, waiting time of appliances and electricity load shifting according to the defined electricity load pattern. Two optimization techniques: binary multiobjective bird swarm optimization and a hybrid of bird swarm and cuckoo search algorithms are proposed to obtain the Pareto front. The main objective of DR is to encourage the consumer to shift the peak load and gets incentives in terms of cost reduction. However, prices remain the same for all the users even if they shift the peak load or not. In this thesis, Game Theory (GT) based Time of Use pricing model is presented to define the pricing strategy for On-peak and Off-peak hours. The price is defined for each user according to the utilized load using coalitional GT. Further, the proposed pricing model is analyzed for scheduled and unscheduled load. In this regards, Salp swarm and rainfall algorithms are used for scheduling of appliances and an aggregated fitness criterion is defined for load shifting to avoid the peak rebound effect. We also proposed the coordination and coalition based Energy Management System-as-a- Service on Fog (EMSaaS_Fog). With the increase in number of electricity consumers, the computational complexity of energy management system is becoming a threat for efficiency of a system in real-time environment. To deal with this dilemma, the utility shifts computational and storage units on cloud and fog. The proposed EMSaaS_Fog effectively handles the coalition among the apartments within a building to maintain balance between the demand and supply. Moreover, we consider a small community, which consists of multiple smart homes. Microgrid is installed at each residence for electricity generation. It is connected with the fog server to share and store information. Smart energy consumers are able to share detail of excess energy with each other through the fog server.
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With the ever-growing demand of electric power, it is quite challenging to detect and prevent Non-Technical Loss (NTL) in power industries. NTL is committed by meter bypassing, hooking from the main lines, reversing and tampering the meters. Manual on-site checking and reporting of NTL remains an unattractive strategy due to the required manpower and associated cost. The use of machine learning classifiers has been an attractive option for NTL detection. It enhances data-oriented analysis and high hit ratio along with less cost and manpower requirements. However, there is still a need to explore the results across multiple types of classifiers on a real-world dataset. This paper considers a real dataset from a power supply company in Pakistan to identify NTL. We have evaluated 15 existing machine learning classifiers across 9 types which also include the recently developed CatBoost, LGBoost and XGBoost classifiers. Our work is validated using extensive simulations. Results elucidate that ensemble methods and Artificial Neural Network (ANN) outperform the other types of classifiers for NTL detection in our real dataset. Moreover, we have also derived a procedure to identify the top-14 features out of a total of 71 features, which are contributing 77% in predicting NTL. We conclude that including more features beyond this threshold does not improve performance and thus limiting to the selected feature set reduces the computation time required by the classifiers. Last but not least, the paper also analyzes the results of the classifiers with respect to their types, which has opened a new area of research in NTL detection.
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As one of the major factors of the nontechnical losses (NTLs) in distribution networks, the electricity theft causes significant harm to power grids, which influences power supply quality and reduces operating profits. In order to help utility companies solve the problems of inefficient electricity inspection and irregular power consumption, a novel hybrid convolutional neural network-random forest (CNN-RF) model for automatic electricity theft detection is presented in this paper. In this model, a convolutional neural network (CNN) firstly is designed to learn the features between different hours of the day and different days from massive and varying smart meter data by the operations of convolution and downsampling. In addition, a dropout layer is added to retard the risk of overfitting, and the backpropagation algorithm is applied to update network parameters in the training phase. And then, the random forest (RF) is trained based on the obtained features to detect whether the consumer steals electricity. To build the RF in the hybrid model, the grid search algorithm is adopted to determine optimal parameters. Finally, experiments are conducted based on real energy consumption data, and the results show that the proposed detection model outperforms other methods in terms of accuracy and efficiency.
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Non-technical losses (NTL) caused by fault or electricity theft is greatly harmful to the power grid. Industrial customers consume most of the power energy, and it is important to reduce this part of NTL. Currently, most work concentrates on analyzing characteristic of electricity consumption to detect NTL among residential customers. However, the related feature models cannot be adapted to industrial customers because they do not have a fixed electricity consumption pattern. Therefore, this paper starts from the principle of electricity measurement, and proposes a deep learning-based method to extract advanced features from massive smart meter data rather than artificial features. Firstly, we organize electricity magnitudes as one-dimensional sample data and embed the knowledge of electricity measurement in channels. Then, this paper proposes a semi-supervised deep learning model which uses a large number of unlabeled data and adversarial module to avoid overfitting. The experiment results show that our approach can achieve satisfactory performance even when trained by very small samples. Compared with the state-of-the-art methods, our method has achieved obvious improvement in all metrics.
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Among an electricity provider's non-technical losses, electricity theft has the most severe and dangerous effects. Fraudulent electricity consumption decreases the supply quality, increases generation load, causes legitimate consumers to pay excessive electricity bills, and affects the overall economy. The adaptation of smart grids can significantly reduce this loss through data analysis techniques. The smart grid infrastructure generates a massive amount of data, including the power consumption of individual users. Utilizing this data, machine learning and deep learning techniques can accurately identify electricity theft users. In this paper, an electricity theft detection system is proposed based on a combination of a convolutional neural network (CNN) and a long short-term memory (LSTM) architecture. CNN is a widely used technique that automates feature extraction and the classification process. Since the power consumption signature is time-series data, we were led to build a CNN-based LSTM (CNN-LSTM) model for smart grid data classification. In this work, a novel data pre-processing algorithm was also implemented to compute the missing instances in the dataset, based on the local values relative to the missing data point. Furthermore, in this dataset, the count of electricity theft users was relatively low, which could have made the model inefficient at identifying theft users. This class imbalance scenario was addressed through synthetic data generation. Finally, the results obtained indicate the proposed scheme can classify both the majority class (normal users) and the minority class (electricity theft users) with good accuracy.
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The illegal use of electricity, defective meters, and a malfunctioning infrastructure are major causes of Non-Technical Losses (NTLs) in electric distribution systems. Although the use of supervised machine learning techniques to detect NTLs has been widely studied, further research is needed in order to address some significant challenges. (i) Given that fraudulent consumers remarkably outnumber non-fraudulent ones, the imbalanced nature of the dataset can have a major negative impact on the performance of supervised machine learning methods. (ii) Given the large number of dimensions present in the time series data used for training and testing classifiers, advanced signal processing techniques are required in order to extract the most relevant information. (iii) The effectiveness of classifiers must be evaluated using meaningful performance measures for imbalanced data. This paper proposes a framework that addresses the three previous challenges. The core of the proposed framework is the application of the Maximal Overlap Discrete Wavelet-Packet Transform (MODWPT) for feature extraction from time series data and the Random Undersampling Boosting (RUSBoost) algorithm for NTL detection. Moreover, our framework is evaluated using an extensive list of performance metrics. Experiments show that the MODWPT combined with the RUSBoost algorithm can significantly improve the quality of NTL predictions.
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The Endesa Company is the main power utility in Spain. One of the main concerns of power distribution companies is energy loss, both technical and non-technical. A non-technical loss (NTL) in power utilities is defined as any consumed energy or service that is not billed by some type of anomaly. The NTL reduction in Endesa is based on the detection and inspection of the customers that have null consumption during a certain period. The problem with this methodology is the low rate of success of these inspections. This paper presents a framework and methodology, developed as two coordinated modules, that improves this type of inspection. The first module is based on a customer filtering based on text mining and a complementary artificial neural network. The second module, developed from a data mining process, contains a Classification & Regression tree and a Self-Organizing Map neural network. With these modules, the success of the inspections is multiplied by 3. The proposed framework was developed as part of a collaboration project with Endesa.
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According to The Brazilian Electricity Regulatory Agency, Brazil reached a loss of approximately US$ 4 billion in commercial losses during 2011, which correspond to more than 27,000 GWh. The strengthening of the Smart Grid has brought a considerable amount of research can be noticed, mainly with respect to the application of several artificial intelligence techniques in order to automatically detect commercial losses, but the problem of selecting the most representative features has not been widely discussed. In this paper, we make a parallel among the problem of commercial losses in Brazil and the task of irregular consumers characterization by means of a recent meta-heuristic optimization technique called Black Hole Algorithm. The experimental setup is conducted over two private datasets (commercial and industrial) provided by a Brazilian electric utility, and it shows the importance of selecting the most relevant features in the context of theft characterization.
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As one of the key components of the smart grid, advanced metering infrastructure brings many potential advantages such as load management and demand response. However, computerizing the metering system also introduces numerous new vectors for energy theft. In this paper, we present a novel consumption pattern-based energy theft detector, which leverages the predictability property of customers' normal and malicious consumption patterns. Using distribution transformer meters, areas with a high probability of energy theft are short listed, and by monitoring abnormalities in consumption patterns, suspicious customers are identified. Application of appropriate classification and clustering techniques, as well as concurrent use of transformer meters and anomaly detectors, make the algorithm robust against nonmalicious changes in usage pattern, and provide a high and adjustable performance with a low-sampling rate. Therefore, the proposed method does not invade customers' privacy. Extensive experiments on a real dataset of 5000 customers show a high performance for the proposed method.
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