# Smart Grid Theft Detection Based on Hybrid Multi-Time Scale Neural Network
## 1 Introduction
In the literature [18], a wide & deep convolutional neural network (wide & deep CNN) was proposed to study electricity theft in a smart grid, and the test results obtained an ROC-AUC of 0.7922. In the literature [19], a combination of graph convolutional network (GCN) and euclidean convolutional neural network (euclidean CNN) was tested and an ROC-AUC of 0.787 was obtained. In the literature [20], the SMOTE technique was first used to generate new data, and then a model combining CNN and LSTM architectures was used for feature extraction and classification, and the test results yielded an accuracy of 92% and ROC-AUC equal to 0.89. In the literature [21], an ADASYN algorithm for processing unbalanced datasets was proposed, after which the balanced data were transmitted to the VGG-16 module for feature extraction, and at last, the extracted features were delivered to the firefly algorithm-based extreme gradient boosting (FA-XGBoost) module for classification, and the test results obtained an accuracy of 93%. RNN is specifically designed for processing time series data, and because electricity consumption of electricity consumers varies over time, it is widely used for detecting electricity theft. In the literature [22], a deep RNN-based electricity theft technique was proposed using a gated recurrent unit (GRU) in the hidden layer, which could better learn the input time series energy consumption information, and the test results obtained an accuracy of 94.2% and false detection rate of 4.2%. In the literature [23], six types of electricity theft were first used to synthesize data to balance the dataset, then LSTM was used to extract the features, and finally GRU with dropout regularization and an added Adam optimizer was used for classification, and the test result obtained an ROC-AUC of 0.9168. In the literature [24], the SMOTE technique was combined with the under sampling technique Tomek Link, followed by feature extraction using KPCA and a bi-directional gated recurrent unit (Bi-GRU) for classification, and the test results yielded an accuracy rate of 80%. In the literature [25], the decomposition of electricity sequences into trend, seasonal, and residual information was input to the Bi-GRU model with the addition of multi-headed attention, which effectively converted the global and fine-grained features of the electricity consumption records, and the test results yielded an ROC-AUC of 0.965. However, existing AI-based electricity theft detection models do not take the following factors into account. The first issue is data imbalance. In a real smart grid, normal electricity consumption data are relatively easy to obtain, but data from electricity theft consumers are rarely collected. Therefore, when training with classification algorithms to diagnose electricity theft, the framework features of a few classes are easily under-extracted, which negatively affects prediction results. Second, most current electricity theft detection approaches are based on one-dimensional electricity consumption data, which does not sufficiently take into account the time series properties of customers' electricity consumption data to obtain the regularity of electricity consumption nor the time correlations of consumers' electricity consumption based on time periods such as weeks and months, resulting in low accuracy and high false detection rates. This paper proposes a new electricity theft detection method to solve the above problems. In the proposed method, a time series generative adversarial network (Time GAN) was used to balance the dataset by synthesizing data from a small number of electricity theft customers. Additionally, a hybrid multi-time scale neural network was designed for the extraction of features. At the the daily time scale, bidirectional long short-term memory (Bi-LSTM) was used to iterate in reverse and extract the global features of customers by using the forward and backward information of the learning consumer sequences.