Application Of Users Behavior Analysis And Visualization Technology Based On Big Data Of Electric Power

With the continuous expansion of the scale of the power grid,coupled with the intelligent terminal equipment installation coverage increased year by year,making the grid business data is an explosive trend of growth,how to use data mining technology,massive power grid data to dig out valuable information is becoming a challenging problem in the current power system analysis.On the other hand,with the development of the market economy,the civil electrical power industry gradually moves to business enterprises.How to analyze and predict the behavior of users,and to provide personalized power services,is a problem that needs to be solved by Power Grid Corp at once.Therefore,the analysis and research of large power data will help to improve the management ability of the Power Grid Corp and the establishment of a strong smart grid.This paper studies the development of large foreign data,and the development of China's big data,the situation of electric power data and the demand analysis of Tianjin electric power company to the business scene are mainly studied.A user behavior analysis and visualization platform based on power big data is proposed,which realizes the function of customer classification and user's electricity consumption prediction.The platform realizes the function of using electricity customer classification and user's electricity consumption forecasting,and displays the results of the analysis.Firstly,through the analysis of target electricity user behavior,power consumption habits and electricity rules,by combining data mining with power customer classification,the design of the overall classification model and the construction of the index system of power customer classification are completed.Based on the combination of clustering algorithm and Hadoop distributed processing framework,a parallel K-means algorithm based on Hadoop is presented.Using Power Grid Corp marketing side data,through the analysis of the power customer's electricity consumption,credit and value creation,etc.,to achieve the function of the customer classification.User's electricity consumption behavior has its own rules,but also by the impact of external factors.Based on the combination of association rule mining algorithm and Hadoop distributed processing framework,a parallel Apriori algorithm based on Hadoop is presented.Combined with the economic,temperature and other factors that can affect the user's electricity consumption,through the analysis of the correlation between these factors and the impact of the user's electricity consumption,to achieve the user's electricity consumption forecasting function.Finally,the mining results are visualized by ExtJS technology.To assist the Power Grid Corp to improve their business ability and management ability,so that the Power Grid Corp can be more targeted to carry out the marketing business and improve their customer service capabilities.