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.

Electricity theft is one of the challenging problems in smart grids. The power utilities around the globe face huge economic loss due to ET. The traditional electricity theft detection (ETD) models confront several challenges, such as highly imbalance distribution of electricity consumption data, curse of dimensionality and inevitable effects of non-malicious factors. To cope with the aforementioned concerns, this paper presents a novel ETD strategy for smart grids based on theft attacks, long short-term memory (LSTM) and gated recurrent unit (GRU) called TLGRU. It includes three subunits: (1) synthetic theft attacks based data balancing, (2) LSTM based feature extraction, and (3) GRU based theft classification. GRU is used for drift identification. It stores and extracts the long-term dependency in the power consumption data. It is beneficial for drift identification. In this way, a minimum false positive rate (FPR) is obtained. Moreover, dropout regularization and Adam optimizer are added in GRU for tackling overfitting and trapping model in the local minima, respectively. The proposed TLGRU model uses the realistic EC profiles of the Chinese power utility state grid corporation of China for analysis and to solve the ETD problem. From the simulation results, it is exhibited that 1% FPR, 97.96% precision, 91.56% accuracy, and 91.68% area under curve for ETD are obtained by the proposed model. The proposed model outperforms the existing models in terms of ETD.

Electricity theft is considered one of the most significant reasons of the non technical losses (NTL). It negatively influences the utilities in terms of the power supply quality, grid’s safety, and economic loss. Therefore, it is necessary to effectively deal with the electricity theft problem. For detecting electricity theft in smart grids (SGs), an efficient and state-of-the-art approach is designed in the underlying work based on autoencoder and bidirectional gated recurrent unit (AE-BiGRU). The proposed approach consists of six components: (1) data collection, (2) data preparation, (3) data balancing, (4) feature extraction, (5) classification and (6) performance evaluation. Moreover, bidirectional gated recurrent unit (BiGRU) is used for the identification of the anomalies in electricity consumption (EC) patterns caused due to factors like family formation changes, holidays, parties, and so on, which are referred as non-theft factors. The proposed autoencoder-bidirectional gated recurrent unit (AE-BiGRU) model employs the EC data acquired from state grid corporation of China (SGCC) for simulations. Furthermore, it is visualized from the simulation results that 90.1% accuracy and 10.2% false positive rate (FPR) are obtained by the proposed model. The results are better than different existing classifiers, i.e., logistic regression (LR), decision tree (DT), extreme gradient boosting (XGBoost), gated recurrent unit (GRU), etc.

A stacking ensemble model (SEM) is proposed in this paper to identify non-technical losses. Three layers make up the proposed model. Data pre-processing is performed at the first layer, where issues of data imbalance, missing values, and data normalization are dealt with. Min-max and a simple imputer are used to handle data normalization and missing values, respectively. Besides, ADASYN and TomekLink are used in a combined form to address the problem of data imbalance. The second layer employs three different machine learning models. The models, also referred to as base classifiers, used at the second layer in the proposed SEM include the following classifiers: random forest (RF), extra tree (ET), and extreme gradient boosting (XGBoost). To accomplish the final classification using the ridge classifier, the output of the basic classifiers is ensembled at the third layer. The ridge classifier is also regarded as the meta classifier. Furthermore, the training and testing of the suggested model is aided by real-time data from the smart grid corporation of China (SGCC). The proposed model’s performance is validated by multiple simulations using various performance indicators and is found to surpass the standalone classifiers in terms of ETD.