Big Data Algorithm for Resource Potential Awareness Response Optimization on the Power User Side Based on IoT Edge Computing

With the rapid development of Internet of Things (IoT) technology and the riguidinguting, the power system is undergoing unprecedented changes. Traditional power system management mainly relies on the centralized data processing mode, which makes it challenging to meet the demand when the data volume increases rapidly and the real-time requirements are high. This paper proposes a big data algorithm based on edge computing of the IoT, aiming at the perception and response optimization problem of resource potential on the power user side. The algorithm aims to improve operational efficiency and reliability of power system through real-time data processing and analysis while reducing energy consumption and cost. This paper combines IoT technology, edge computing, and extensive data analysis methods to collect power usage data in real-time by deploying intelligent sensing devices on the user side and conducting preliminary data processing and analysis on edge nodes. The algorithm uses machine learning and optimization algorithms to deeply analyze the data, identify the potential of user-side resources, and automatically adjust the power usage strategy according to the analysis results to achieve the optimal allocation of resources. By setting up a simulation environment, the proposed algorithm is tested. Experimental results show that the algorithm can effectively identify the potential of power resources on the user side and realize the dynamic balance of power demand by optimizing the response strategy. In comparative experiments, compared with traditional methods, this algorithm can reduce energy consumption by about 20% and improve power usage efficiency by about 15%.