Power grid user classification method based on load characteristic index weighted clustering algorithm

Abstract

The invention discloses a power grid user classification method based on a load characteristic index weighted clustering algorithm. Load curve clustering is introduced, the problem of high complexityof an existing algorithm is solved, load characteristic indexes are built, the dimension of a traditional AP (affinity propagation) algorithm is reduced to improve the efficiency of the algorithm, similarity calculation is improved by weight, and convergence judgment is performed according to DB (Davies-Bouldin) indexes. In order to overcome the shortcomings of subjective weight, characteristic index contribution degree evaluation rules are designed, the weight of the load characteristic indexes is determined objectively and adaptively by an entropy weight method to measure the distinction degree of the characteristic indexes for clustering results, weight assignment reasonability is improved, and accuracy of user classification results is finally improved.

Description

technical field [0001] The present application relates to the technical field of power consumption data analysis, in particular to a method for classifying power grid users based on a load characteristic index weighted clustering algorithm. Background technique [0002] Classifying users according to their electricity consumption is of great significance to the power company. The power company can analyze the electricity consumption of different types of users according to the type of users, and provide users with better services. Existing In technology, clustering algorithms are often used to classify users. Many scholars have conducted research on different clustering algorithms to explore the clustering effect on load curves. Different algorithms differ according to their processing goals and data types, and are usually divided into two types: direct clustering and indirect clustering. Direct clustering is a technology that directly processes load data, including Kmeans,...

AI Technical Summary

Problems solved by technology

However, the complexity of the algorithm itself is relatively high. When dealing with multi-dimensional large amounts of data, the AP clustering algorithm often takes a long time to calculate.

In addition, the user's electricity consumption data is often weighted before clustering, but the weight assignment method in the prior art is an expert judgment method, which is subjective and has low efficiency

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