Data Research And Application Of Electricity Payment Behavior Based On K-means

Under the new direction of the development of smart grid power marketing,grid companies must accurately locate high-quality customers,change the original thinking mode,scientifically allocate service resources,and view marketing from the perspective of ordinary enterprises.Research and analyze the behavior data of power users,accurately locate the needs,consumption habits,behavior trends and psychological changes of users,it is of great significance for the national grid and other power companies to open up the electricity sales market,improve the service quality of domestic users,and improve the core competitiveness of domestic and overseas markets.Firstly,perform pre-processing work such as cleaning and transformation of historical power user behavior data accumulated in the existing marketing business application system of the power grid and 95598 customer service system to obtain a standardized data set for removing outliers and null values,and analyze the user value model of power payment data in the absence of circumstances,based on the traditional RFM consumer value analysis model,combined with the characteristics of power user payment behavior,a CRFMO power payment user comprehensive value evaluation model was constructed,and the minimum-maximum standardization of CRFMO characteristics was adopted.Based on this CRFMO characteristic value model,K-means user value grouping of the algorithm.Secondly,in order to optimize the initial clustering center randomness problem of the K-means clustering algorithm,combines the DPC density peak algorithm and proposes an optimized KD-means clustering algorithm.By calculating the similarity matrix based on the weighted Euclidean distance,we obtain the local density and high-density distance of all sample points are used to obtain the cluster center selection index,so as to determine the initial cluster center point.By comparing the clustering evaluation indexes,the optimized KD-means model has better clustering effect.For the user group obtained by clustering,analyze the clustering center value of each CRFMO feature,draw a feature radar chart,classify the value of power users according to the difference in performance characteristics of different user categories,subdivide and position the user value,and summarize each user group features and response strategies.Finally,based on the KD-means clustering algorithm for the value grouping model of power payment behavior,a prototype system for data analysis of power user payment behavior is designed and developed to realize the visualization of power user grouping results.Through systematic data analysis and presentation,the characteristics of power payment users are displayed intuitively,which provides a theoretical basis for power companies to carry out market purchase and sale side reforms and formulate efficient power sales marketing strategies,effectively helping power companies achieve accurate marketing strategies.