Electric Power Gateway Metering Monitoring and Analysis Platform Based on Random Forest Algorithm

Under the concept of “emerging Internet of Things”, computing and intelligence have become the guideline for the development of smart grids. On the one hand, communication technology is used to realize large-scale communication between underlying electronic devices and cloud platforms; on the other hand, with the gradual development of artificial intelligence and data mining technology, the value of power grid data must be further indexed. The main purpose of this paper is to design and study the power gateway metering monitoring and analysis platform based on the random forest (RF) algorithm. This question is mainly aimed at the problem that most devices on the power user side cannot directly transmit data to the cloud platform, and designed and implemented a power gateway device system. In addition, in terms of power data analysis, relying on machine learning theory, through the research and analysis on the detection of abnormal user behavior, a multi-classifier combination-based abnormal power behavior detection model is proposed. In this paper, the function and performance of the researched and designed intelligent power distribution monitoring system are tested. The accuracy of its functionality and technical indicators is verified in the laboratory environment, and it is found that for a single classifier, the RF algorithm is more suitable for the detection of abnormal electrical behavior, which meets the design requirements.