Research on Electricity Consumption Behavior of Electric Power Users Based on Tag Technology and Clustering Algorithm

With the continuous development of the demand of electric power users, the analysis of users' electric power consumption(EPS) is becoming more and more important. The frequency of data acquisition and the dimension of data analysis are increasing, which makes the analysis of EPS behavior more precise. The development of user tag and portrait technology brings a more intuitive and concise expression to the analysis of user's EPS. Based on massive user archives, power load and EPS data, considering the user's power consumption characteristics and influencing factors, a user behavior tag library is built, and k-means algorithm is used to tag clustering to achieve different types of EPS behavior. The tag clustering results of 2000 industrial and commercial users show that the selected tags of EPS behavior is reasonable and the clustering algorithm is effective. The research results of this paper can provide powerful data support for power companies to understand the user's EPS habits, mining user's electric power needs and improve the service level.