Research On Smart Grid User Behavior Analysis And Orderly Power Consumption Decision Method

In recent years,the sustained development of China's economy has led to an increasingly significant increase in demand for electricity,the peak of the power supply and demand imbalance problem is particularly prominent.In response to the shortage of electricity supply in China,the implementation of orderly electricity is still an important means of load management in China recently.In order to better implement the orderly power management measures,based on the user behavior analysis,considering the user behavior of electricity consumption characteristics to develop the order of electricity strategy,to enhance the power users of electricity satisfaction,optimization of power resources allocation is important significance.China's power consumption information collection system to build,making the power companies can access the user's electricity load data is increasingly large.Massive power consumption data to the user behavior analysis has brought new opportunities and challenges.Aiming at this problem,this paper presents a method to encode the user's typical electrical load.Firstly,an adaptive k-means algorithm based on dynamic separation of clustering centers and optimization of clustering centers are used to extract the typical load shape of users.Then the typical load is encoded and the load attribute values are recorded to establish a universal user Load dictionary.Finally,based on the built-up dictionaries,the user's power behavior is simulated and analyzed by MATLAB.The average maximum load of user and the stability of user's behavior are extracted,and the potential of different types of users participating in orderly power consumption is analyzed.At the same time,the current orderly strategy in the development of electricity consumption for the user on weekdays and weekdays,the difference is often only for the number of simple assumptions,which may lead to weekends to take the wrong orderly electricity strategy,greatly reduced Demand Side Management Level.In this paper,we use the Pearson correlation coefficient between weekday weekday and weekday load data to construct the eigenvalues.The cluster number is determined by clustering validity index,and the power behavior of users during weekends is studied.By MATLAB simulation,the user is divided into four categories,a detailed analysis of the behavior of different types of user characteristics,and thus can be more comprehensive and accurate grasp of the user's typical power behavior.Finally,the analysis of the above two kinds of user behavior analysis method in order to formulate the application of electricity strategy.Firstly,the potential value of users participating in orderly electricity consumption is analyzed by the method of user load coding and behavior analysis based on dynamic adaptive K-meansclustering.Secondly,the power consumption behaviors of users participating in the orderly use of electricity are analyzed in weekdays and weekdays.,In order to develop a more rational and orderly power strategy.In this paper,a typical user load dictionary is used to solve the problem of large-scale user load data processing,taking into account the typical user load difference between weekday and weekday,based on the user It is of great significance to optimize the power resource allocation by improving the power users' satisfaction of electricity consumption.