A BiLSTM-based Industry Abnormal Electricity Consumption Warning Model in the Context of Electricity IoT Edge Cloud

Abstract

Various emerging technologies and the development of Internet of Things (IoT) technology make people’s life gradually become intelligent. Electricity IoT is the application of IoT technology in the smart grid, which is increasingly concerned by the global technical staff in the current rapid development of technology. However, with the increase in demand for electricity in various industries, as well as the impact of economic and epidemic factors, the problem of abnormal electricity consumption in industries has become increasingly prominent. Abnormal electricity consumption in the industry can disrupt the normal order of electricity supply and consumption in the grid, leading to huge economic losses. In this regard, many scholars have conducted relevant research and achieved certain results. For the problem of abnormal electricity consumption in the industry, this paper proposes an intelligent early warning model based on biidirectional long short-term memory (BiLSTM) network, which combines multiple factors to determine abnormal electricity consumption for real-time warning, and uses edge cloud structure to make data collection more efficient and comprehensive. The experimental results show that the method proposed in this paper has obvious advantages in the abnormal electricity consumption detection problem, with better real-time and reliability.