Analysis And Research On User Peaking Potential Based On Data Mining

In recent years,electricity demand across the country has maintained rapid growth,power shortages have occurred from time to time,the pressure on power grid operation has continued to increase,and many places in southern my country have introduced "power curtailment" measures at the end of 2020.Although the orderly use of electricity can guarantee the stability of the order of electricity consumption,it will inevitably have a certain degree of impact on industrial production.If only by means of expanding the installed capacity of generators,it will put greater pressure on technology and economy.Therefore,in the environment of the continuous development of smart grids,more emphasis will be placed on the analysis and research of user-side power consumption behavior characteristics,personalized services will be provided to different residential customers,and customers will be guided scientifically and reasonably to use energy,so that the power-saving potential on the user side will be fully utilized Enhance the capacity of residential grid load control,effectively cut peaks and fill valleys,realize the interaction and balance between the supply side and the demand side,improve the reliability of system operation,and promote the implementation of demand response activities,enhance users’ awareness of power saving,and make profits at the same time It will also help achieve the goals of "carbon peak" and "carbon neutrality",ensure the sustainable economic and social development,and achieve a win-win situation for the government,enterprises,power grid companies,and residents.This paper first uses particle swarm algorithm to improve the traditional K-means clustering algorithm,and then realizes the accurate classification of the electricity consumption behavior of residential users,and can formulate corresponding service methods according to the needs of users for various energy service services;secondly,it is multi-dimensional Refinedly explore the influencing factors of users’ peak shaving potential,characterize their electricity consumption attributes,establish a universal customer energy label identification model,and build a sample library of residents’ electricity consumption characteristics,which can provide reference for personalized value-added services for different residents,To provide data support for application scenarios such as the formulation of energy use policies,energy use services,and business activities operation guidance;then introduce the generalized gray absolute correlation and principal component analysis to select the best and reduce the dimensions of the above indicators to avoid the problem of data attribute redundancy,And then improve the quality of the input indicators of the prediction model,and use three algorithms to build potential customer matching models,and evaluate the three prediction models to obtain the optimal prediction model,which can provide data support for demand response activities and locate service targets Customer base,reduce the cost of event implementation,combine with the intelligent energy service specimen library,and provide differentiated and personalized services for residential electricity customers based on the energy use attributes of each user,improve user participation and satisfaction,and enhance residential electricity customers The stickiness of the company brings value-added services and develops the integrated energy service market;finally,the GUI user interface of the demand response prediction model is designed to facilitate the operation of the user.