# Smart Grid Theft Detection Based on Hybrid Multi-Time Scale Neural Network
## 1 Introduction  
Smart grids incur losses from both technical and non-technical sources in electricity distribution and transmission. The former stems from power dissipation in transmission and distribution lines, transformers, and other electrical equipment, which are inherent aspects of power systems. The latter, however, is primarily caused by electricity theft perpetrated by malicious customers [1]. Electricity theft results not only in significant financial losses for grid companies, but also in increased electricity demand, overloading, and other security concerns. Moreover, it poses a serious threat to public safety, including the risk of fires and electric shocks to customers [2]. As such, this study carries significant economic value and plays a critical role in addressing public safety issues. In the past, the main means of checking electricity theft was by power grid inspectors checking the site and manually analyzing the electricity consumption records. However, this method was time-consuming and caused a great deal of wasted labor and material resources. Recently, the speedy growth of IoT technology and artificial intelligence has enabled the development of anti-theft technologies that can be divided into the following two types: hardware-based and data-driven [3]. Hardware-based methods rely primarily on the placement of sensors in relevant areas, which mainly change the design and architecture of smart meters, to better prevent illegal persons from stealing electricity; however, the high operating and maintenance costs of dedicated hardware have limited further development in this area [4; 5; 6]. The growing availability of smart meters provides a massive volume of high-precision, multi-dimensional, and fine-grained electricity consumption data, which provides us with the possibility of carrying out data-driven identification of electricity theftby electricity consumers [7]. Data-driven approaches are further classified into system state-based [8; 9; 10], game theory-based [11; 12] and artificial intelligence-based methods, using models such as support vector machine (SVM), artificial neural network (ANN), convolutional neural network (CNN), and recurrent neural network (RNN). SVM is a classical model frequently used in the detection of electricity theft. In the literature [13], the synthetic minority oversampling technique (SMOTE) was first used to balance the dataset, then the kernel function integrated with principal component analysis (KPCA) was used to extract features, and finally, SVM classification was used, which was tested to obtain an accuracy of 85% and an area under the ROC curve (ROC-AUC) of 0.89. In the literature [14], the raw data were first input into a decision tree (DT) to calculate the expected electricity consumption of users at a specific time, and then they were input to the SVM classifier along with other features, and the accuracy rate obtained after testing was 92.5% and the false alarm rate was 5.12%. ANN is also used to detect electricity theft. In the literature [15], the time domain, frequency domain, and combinatorial domain features of electricity consumption data were obtained and used as input to a deep neural network (DNN) for categorization, and principal component analysis (PCA) with diminished subspace and a minimum redundancy maximum relevance (mRMR) scheme was performed to identify significant features, with an ROC-AUC of 0.92 obtained. In the literature [16], a hybrid deep neural network composed of an LSTM network and multilayer perceptron (MLP) was proposed to detect anomalous features in smart meters, and the tested ROC-AUC was 0.84. In the literature [17], a hybrid data resampler was proposed for dataset balancing using a simulated annealing algorithm, regularization method, and jump connection for optimizing ANN to improve the training and classification capability of the model, and the accuracy obtained after testing was 99.6%. CNN is widely used in detecting electricity theft as deep learning advances.