Analysis Method of Power Consumption Characteristics of Residents in Low-Voltage Stations Based on Clustering Algorithm

With the development of smart grid, the power data resources increase rapidly, and the potential value of data is gradually explored. This paper based on clustering algorithms, clustering massive amounts of low-voltage station users' electricity data, and analysing each user group by clustered electricity consumption behaviour. Classify the low-voltage station according to the electricity consumption behaviour and extract the characteristic parameters of the low-voltage station. Then comprehensively considering the users' electricity consumption behaviour, and characteristic parameters of the typical low-voltage station, the support vector regression (SVR) is used to construct the typical low-voltage station model. The improved particle swarm optimization is used to optimize the SVR parameters. Finally, different low-voltage stations in Tianjin are selected to verify the effectiveness of the proposed model.