**3. Proposed Approach**

In this section, we propose our electricity theft detection scheme. We introduce the data preprocess at first. Then, we propose a neural network structure based on TextCNN, which consists of convolutional layers, pooling layers and fully-connected layers. Finally, we propose the data augmentation method to increase the amount of electricity theft data for the balance of the training dataset. The total framework of the proposed electricity theft detection is demonstrated in Figure 5.

**Figure 5.** Proposed electricity detection scheme.

As shown in Figure 5, the raw data collected by smart meters gets through the data preprocess at first. Then, we divide it into the training dataset and the test dataset. If the training dataset is imbalanced, we utilize the proposed data augmentation method to balance it. Finally, we train the proposed network on the training dataset and validate the effect on the test dataset. The metrics used for training and testing are introduced in Section 4.3. It should be noted that the training process is supervised learning which requires labeled datasets.
