Electricity-Theft Detection Based on Optimized Deep Learning Model in Smart Grid

As the digital transformation of China's power industry is underway, digital analysis plays a critical role in many electricity businesses. One of the important uses of digital analysis is to detect electricity theft from massive electricity consumer data. Accurate electricity theft detection is vital since its profit for the smart grid is straightforward. However, the consumers' stochastic behaviors make the electricity consumption data a complex signal. Moreover, huge consume data increase the difficulty of detection. Therefore, traditional data analysis methods are inadequate to deal with the aforementioned challenge. In the literature, there has been consistent interest in deep learning when dealing with this kind of problem. But accurate detecting models are needed. In this paper, a novel optimized deep learning model is proposed. Combining the data pre-processing method and fine-tuned neural network, the proposed model achieves a high-level performance. The accuracy of the proposed model is evaluated over a massive real-world electricity consumer dataset. Compared with several well-known deep learning models, the proposed model shows a better performance.