基于机器学习算法的电力用户参与负荷调节特征模糊聚类OA

In order to achieve effective regulation of power load,the current method of load clustering based on user types considers the similarity of power consumption characteristics of power users under the distribution of load values.This inherent mode easily ignores user characteristics with insufficient features,resulting in the loss of load data and the inability to comprehensively cluster the power consumption behavior of power users,distinguish subtle differences in load forms,and have significant limitations.Based on this,a fuzzy clustering method for power users'participation in load regulation features based on machine learning algorithms is proposed.Firstly,use low rank tensors to complete the missing load data in the model;Secondly,the fuzzy C-means algorithm is used to cluster the relevant data describing the load characteristics of users in the load feature data;Again,using Self-Organizing Map(SOM)neural network,based on the data of influencing factors of user electricity consumption behavior,obtain secondary clustering results that can describe the adjustable potential of user participation in load regulation;Finally,a reverse correction strategy is introduced to adjust the position of the clustering center and the allocation of clustering members,correct the results of the first and second rounds of clustering,effectively distinguish subtle differences in load morphology,and output a comprehensive clustering result of load characteristics that can describe the adjustable ability of participating loads.The test results show that this method effectively completes missing data completion,with clustering separation and effectiveness functions values above 0.9,and load characteristic coverage rates above 90%.It can well describe the changes in load and provide reliable basis for load adjustment.