Peak clipping and valley filling-oriented electricity utilization behavior analysis method and system

Abstract

The invention discloses a method for analyzing power consumption behavior oriented towards peak shaving and valley filling, which includes: clustering the load data of the main network in the historical period using the maximum expectation algorithm, and obtaining the clustering results of the load on different dates in the historical period; The point-by-point load data of users included in each group is subjected to a parallel hard clustering algorithm to obtain the grouping results of users under different date groups; compare the load characteristics of the main network date group with the grouping results of users under the date group Load characteristics, to obtain the matching results of peak-shaving and valley-filling patterns of users on different dates in history and the target objects of peak-shifting electricity consumption. The invention can analyze the electricity consumption behavior of users under massive data, thereby improving the accuracy of user behavior positioning, and providing decision-making reference for more effective customer service, improvement of customer satisfaction, and reduction of operational risks. The invention also discloses an electricity consumption behavior analysis system oriented towards peak shaving and valley filling.

Description

technical field [0001] The present invention relates to the technical field of electricity consumption analysis, in particular to an analysis method and system for electricity consumption behavior oriented towards peak shaving and valley filling. Background technique [0002] With the deepening application of the electricity collection system and the continuous improvement of customer basic information, there are more and more types of data, and the degree of refinement of the data is getting higher and higher. The power information data is showing an explosive growth trend. The era of big data in the power industry has passed arrival. At present, these data are mainly used to assist decision-making in the internal marketing business of power companies, and the data value of massive power consumption information has not been fully tapped and reflected. [0003] At present, most of the following methods are directly used for the analysis of power consumption behavior: [00...

AI Technical Summary

Problems solved by technology

The expert experience method is a simple and convenient method, but it also has obvious shortcomings and deficiencies: it is greatly affected by human subjective factors, such as the professional level and authority of experts, etc., which may affect the accuracy of the analysis conclusion degree

[0007] To sum up, the traditional method is too simple and can analyze electricity consumption behavior to a certain extent, but the application effect is not very ideal in the case of massive data

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