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The theft of electricity is regarded as a global problem which creates negative impacts for both electricity users and utility companies. The economic development of utility companies gets destabilized which further leads to electric hazards, thereby increasing the energy cost. Numerous methods are utilized for substantial detection of electricity theft, but these approaches consume more time and are inefficient and expensive. Electricity theft detection also uses artificial intelligence techniques like deep learning and machine learning. Despite innovative and remarkable characteristics of these approaches, their performance is unsatisfactory. Taking these aforementioned issues into consideration, a cascaded region-based convolutional neural network with a cascade of specialized regressors is proposed in this work for efficient detection of electricity theft. The proposed classifier determines the close false positives for adjacent stage training enabling the generation of high quality detection of electricity theft. Initially, pre-processing which combines data interpolation and data normalization is carried out for the process of recovering missing values. An adaptive synthetic technique is utilized to address class imbalance issue owing to unbalanced data. In order to extract relevant features, a hybrid whale optimized chicken swarm algorithm is used which selects the accurate features thus performing the effective modelling of obtained electrical parameters. In comparison with existing approaches, the proposed work generates optimized results for performance metrics values with an accuracy of 94.3%, F1-Score of 94.58%, and precision of 94%. [ABSTRACT FROM AUTHOR]

One of the crucial issues for power grids in strengthening the urbanization around the world is imbalance between supply and demand, which leads the users to consume electricity in an anomalous manner without paying for it. Electricity theft plays a pivotal role in cutting down on the electricity bills. The existing data-oriented approaches for electricity theft detection (ETD) in the smart cities have limited ability to handle noisy high-dimensional data and features' associations. These limitations raise the misclassification rate, which makes some of the approaches unacceptable for electric utilities. A new twofold end-to-end methodology is proposed for ETD. In the first fold, it groups the similar electricity consumption (EC) cases through grey wolf optimization (GWO)-based clustering mechanism; clustering by fast search and find of density peaks (CFSFDP), we named it GC. In the second fold, a new relational stacked denoising autoencoder (RSDAE)-based semi-supervised generative adversarial network (GAN), termed as RGAN, is used for ETD. The combined methodology is named as GC-RGAN. In the methodology, RSDAE acts as both feature extraction technique and generator sub-model of the proposed RGAN. The proposed methodology utilizes the advantages of clustering, adversarial learning and semi-supervised EC data. Besides, to validate the effectiveness of the proposed solution, extensive simulations are performed using smart meter data. Simulation results validate the excellent ETD performance of the proposed GC-RGAN against existing ETD schemes, such as random forest and semi-supervised support vector machine. In comparison, GC-RGAN covers the ETD score of 98% that shows its suitability for real-world scenarios. The proposed solution has extraordinary performance for ETD as compared to traditional solutions, which shows its superiority and usefulness for real-world applications. [ABSTRACT FROM AUTHOR]

As an important edge device of power grid, smart meters enable the detection of illegal behaviors such as electricity theft by analyzing large-scale electricity consumption data. Electricity theft poses a major threat to the economy and the security of society. Electricity theft detection (ETD) methods can effectively reduce losses and suppress illegal behaviors. On electricity consumption data from smart meters, ETD methods always train deep learning models. However, these methods are limited to extract different electricity consumption characteristics between independent users, and the pattern differences between users cannot be actively learned. Such difficulty prevents ETD further performance improvement. Therefore, a novel ETD method is proposed, which is the first attempt to apply supervised contrastive learning for electricity theft detection. On the one hand, our method allows the detection model to improve its detection performance by actively comparing users' representation vectors. On the other hand, in order to obtain high-quality augmented views, largest triangle three buckets time series downsampling is adopted innovatively to improve model stability through data augment. Experiments on real-world datasets show that our model outperforms state-of-the-art models. [ABSTRACT FROM AUTHOR]

Electricity theft is a major cause of non-technical loss (NTL) in smart grids. However, existing research on electricity theft detection (ETD) lacks a generalized analysis of the characteristics of theft behaviors and fails to effectively address the problem of unbalanced sample distribution; the electricity theft features they recognize are also singular. To address these problems, a novel approach consisting of a sample augmentation mechanism based on convolutional transformer-Wasserstein generative adversarial networks (CT-WGANs) and an electricity theft detection scheme using bridged multiscale convolutional neural network-bidirectional gate recurrent units (MCNN-BiGRUs) is proposed in this paper. First, the generalized characteristics of electricity theft behavior in multiple time dimensions are analyzed, and the data slices are constructed. Then, with the aim of reducing the influence of unbalanced sample distribution, CT-WGAN, which focuses on the generalized characteristics of electricity theft in various time dimensions, is designed and has better sample generation capability. Finally, a bridged MCNN-BiGRU is proposed to recognize the temporal, transition, and persistence characteristics of electricity theft to improve the efficiency of ETD. Experimental results on the State Grid Corporation of China (SGCC) and Irish Smart Energy Trial (ISET) datasets show that the proposed approach outperforms traditional schemes in terms of the area under curve (AUC), precision, recall, f1-score, and accuracy. [ABSTRACT FROM AUTHOR]

Electricity theft seriously affects the stability of the power system, causing damage to both legitimate users and power companies. Traditional data-driven electricity theft detection methods rely on manually designing the feature representation of electricity data, and often ignore the time-series nature, which are difficult to be applied to well extract the features of high-dimensional data. To solve these problems, this paper proposes a method, combining white noise test and Seasonal and Trend Decomposition using LOESS, to adaptively obtain the potential period range of time-series, and then determines the period to be decomposed by using prior tools. In the classifier construction part, convolutional neural networks with different kernel sizes are used to extract medium and short-term features, and residual networks are used to extract long-term features. Additionally, the proposed method here is comprehensively compared with other general classifiers on the public electricity theft dataset, and the results, that the multiple metrics of the proposed method are significantly improved compared with the baseline models, demonstrate superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Nowadays energy theft in smart grids has led to severe economic loss to utility companies and countries across the world. Malicious users who illegally steal electricity from the distribution centre without getting noticed lead to unplanned power loss and blackouts in many developing countries. In order to prevent and find the malicious users who steal electricity, a system must be introduced in such a way that the malicious users will be identified easily in a large neighbourhood area network. In this work, to detect energy theft, a decision-making system using the Hadoop multi-cluster model with interpolation search algorithm has been proposed to calculate the actual energy consumed by every user and classify the user as the honest user or malicious user. The proposed interpolation-based approach is lightweight in its operation, which reduces the time complexity for predicting the malicious users in the NAN. The simulation results indicate that the proposed algorithm performs better than the existing approaches in terms of number of inspection steps, execution speed, detection accuracy and false-negative rate. [ABSTRACT FROM AUTHOR]