Distributed double-layer clustering analysis method based on feature index dimension reduction

Abstract

The invention relates to a distributed double-layer clustering analysis method based on feature index dimension reduction. The method belongs to the field of power system user response clustering algorithms, and comprises the following steps: S1, collecting intelligent electric meter data, transmitting the intelligent electric meter data to a nearest local site, decomposing a large number of loadcurves into a plurality of small-scale independent sub-data according to the sites to which the load curves belong, and further dividing the sites with more load curves; s2, carrying out data dimension reduction on the load data decomposed to each station, carrying out primary clustering by adopting a clustering algorithm with relatively low complexity, and clustering different customers in the region to obtain a clustering result; s3, forwarding clustering results (only uploading the clustering center without uploading all data) obtained from different local sites to a global data center forsecondary clustering, and obtaining a final clustering result; and S4, the global data center feeds back a global clustering result to each local site, and performs user power consumption behavior analysis.

Description

technical field [0001] The invention belongs to the field of user response clustering algorithms in electric power systems, and relates to fast and accurate realization of user response clustering by designing a distributed two-layer clustering framework and improving corresponding algorithms. Background technique [0002] With the advancement of science and technology and the improvement of power grid informatization level, a large amount of electricity consumption data has been accumulated on the user side. Mining the valuable information hidden in massive data will help power grid companies understand users' electricity consumption habits, provide targeted services, and improve energy utilization efficiency. However, due to the huge amount of power system data and the increase of redundant features, the calculation efficiency is greatly reduced. [0003] The research on big data clustering of power load is still in its infancy, and there is no unified standard. At prese...

AI Technical Summary

Problems solved by technology

However, the existing divide-and-conquer framework is either not fully self-adaptive, or not suitable for power load big data clustering, and the construction of the power data divide-and-conquer framework needs to be further improved

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