Power consumer clustering power utilization behavior characteristic analysis method based on load decomposition

Abstract

The invention relates to a power consumer clustering power utilization behavior characteristic analysis method based on load decomposition. According to the method, considering that the proportion ofmeteorological sensitive loads such as temperature in the total power load becomes larger and larger, in order to analyze the load of a power consumer more finely, the total power load is decomposed into a basic-level load and a seasonal load, and then a fuzzy C-means algorithm is used for performing clustering analysis on the two levels of loads respectively; the method has the following steps: selecting typical users for each cluster, comprehensively considering a plurality of common screening methods, and finally selecting each type of typical users by adopting a grey correlation degree method; and finally, calculating important characteristic indexes of each type of typical users to analyze the magnitude of the load regulation and control potential of the typical users, and further classifying the power users according to the magnitude of the regulation and control potential of the typical users so as to finally formulate more detailed demand side regulation and control measures.

Description

technical field [0001] The invention relates to the field of analysis methods for power user clustering power consumption behavior characteristics, in particular to an analysis method for power user cluster power consumption behavior characteristics based on load decomposition. Background technique [0002] The cluster analysis of power users based on load decomposition includes five basic contents: 1) data acquisition and preprocessing; 2) load decomposition; 3) fuzzy C-means clustering (FCM) analysis; 4) typical user screening based on gray relational degree; 5) Analysis of regulatory potential. Together, they constitute the process of analyzing the characteristics of power user clustering power consumption behavior as follows: figure 1 shown. This method first needs to obtain the corresponding power user load data and perform preprocessing (removing users with a large number of blank values ​​and obviously wrong data); considering that meteorological factors are increas...

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Problems solved by technology

Although the clustering results can also analyze the power consumption behavior of various users, there may be a situation where the total load belongs to the same category, but if the load decomposition is adopted, the difference between the basic load and the seasonal load is relatively large. big, not even in the same class

Therefore, if we continue to cluster power users according to the traditional non-decomposition method, we cannot conduct detailed analysis on power users, that is, we cannot guarantee that the demand-side control measures formulated in this way are effective.

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