Research on power customer segmentation based on big data of intelligent city

With the vigorous development of intelligent city, it has been an important task that we need to consistently revitalize owned information, make full use of the incremental data, to provide unified data service for intelligent application in various fields. This paper proposes an improved power customer segmentation model, which uses an optimized algorithm that is based on the basic data of the intelligent city and the internal data of the electric power enterprise. This model attach importance to the customer's change tendency on the basis of maintaining the original clustering distance, making the result of clustered classification more detailed and more advanced. At the same time, the quality of clustering center is improved and the convergence rate of clustering is improved by using the pre-clustering and average clustering center linear prediction. The simulation results show that this model provide a better classification way for power customers, which has higher classification quality and faster speed.