Edge Computing-Driven Optimization of Electricity Consumption Information Collection

As the scale of the power system enlarges, the current system of collecting power consumption information has the disadvantages of delays in transmitting information, excessive bandwidth pressure, and the lack of real-time information processing capability. In order to alleviate the aforementioned issues, this paper provides an edge computing method to enhance the system of collecting power consumption information. The edge computing concept is to execute the preprocessing, analysis, and decision-making functions of the data near the source end at the edge nodes to decrease the center server load and enhance the real-time nature and reliability of the system. This paper initiates the explanation of the use cases of edge computing to collect power consumption information and verifies the optimization strategies of edge computing through specific examples. In practical use cases, edge nodes execute functions such as anomaly detection of power consumption and fault alarms through real-time monitoring and analysis of the information, significantly decreasing the amount of transmitted information and the system's response time. The results prove that the edge computing optimization strategy has superior performance in the efficiency of transmitting information, the ability to process real-time information, and system stability, providing a new technical means to the intelligence and efficiency of the system of collecting power consumption information.