Electricity Theft Detection in Smart Grids Using a Hybrid BiGRU–BiLSTM Model with Feature Engineering-Based Preprocessing

Abstract

In this paper, a defused decision boundary which renders misclassification issues due to the presence of cross-pairs is investigated. Cross-pairs retain cumulative attributes of both classes and misguide the classifier due to the defused data samples’ nature. To tackle the problem of the defused data, a Tomek Links technique targets the cross-pair majority class and is removed, which results in an affine-segregated decision boundary. In order to cope with a Theft Case scenario, theft data is ascertained and synthesized randomly by using six theft data variants. Theft data variants are benign class appertaining data samples which are modified and manipulated to synthesize malicious samples. Furthermore, a K-means minority oversampling technique is used to tackle the class imbalance issue. In addition, to enhance the detection of the classifier, abstract features are engineered using a stochastic feature engineering mechanism. Moreover, to carry out affine training of the model, balanced data are inputted in order to mitigate class imbalance issues. An integrated hybrid model consisting of Bi-Directional Gated Recurrent Units and Bi-Directional Long-Term Short-Term Memory classifies the consumers, efficiently. Afterwards, robustness performance of the model is verified using an attack vector which is subjected to intervene in the model’s efficiency and integrity. However, the proposed model performs efficiently on such unseen attack vectors.

Electricity Theft Detection for Smart Homes: Harnessing the Power of Machine Learning With Real and Synthetic Attacks

“…One of the key challenges in deploying machine learning classifiers for ETD in smart homes is the pervasive issue of data imbalance [22], [23], where the volumes of normal and abnormal samples exhibit substantial disparities. While benign samples are readily accessible through historical data, attack or theft samples are often scarce, and, in some cases, entirely absent for certain customers.…”

Data-Driven Insights: Boosting Algorithms to Uncover Electricity Theft Patterns in AMI

“…Similarly, Darshana et al [29] use a gradient boosting-based Weighted Feature Importance (WFI) model for feature elimination, risking the oversight of predictive feature interactions. Shoaib et al [30] introduced innovative feature engineering methods, yet they often require extensive computational resources, raising concerns about scalability and efficiency. Pamir et al [31] apply autoencoders for feature extraction from historical data, trading off interpretability for sophistication.…”

Design of Intelligent Neuro-Supervised Networks for Brain Electrical Activity Rhythms of Parkinson’s Disease Model

“…In [ 22 ], swarm intelligence is exploited for the identification of fractional order nonlinear autoregressive exogenous systems through established strength of particle swarm optimization (PSO). In [ 23 ], a hybrid bi-directional gated recurrent unit (BiGRU) and bi-directional long-term short-term memory (BiLSTM) are presented for electricity theft detection in smart grids with preprocessing through feature engineering. Before using the BiGRU and BiLSTM for classification, the data imbalance issue is solved using a K-means minority oversampling scheme such that the balanced data are given as an input to the BiGRU and BiLSTM models for better classification accuracy.…”