Electricity fee sensitive user analysis method based on stacking and bagging algorithms

Abstract

The invention discloses an electricity fee sensitive user analysis method based on stacking and bagging algorithms. The electricity fee sensitive user analysis method comprises steps of using an optimal zone algorithm to solve problems of nonequilibrium and intolerance and construct a training set and a testing set according to electricity fee sensitive classification targets and on the basis of multiple core business indexes of an electricity fee sensitive original data table, further subdividing related core business structuralized characteristics based on electricity fee sensitivity, using a stacking method to construct an electricity sensitive primary model on the basis of nonstructural text characteristics of particles and word frequency statistics, generating extended stacking characteristics for each sample, combining characteristics of the two as an integral input, using a bagging algorithm and a vote algorithm to construct an electricity fee sensitivity secondary model on the training set and the testing set, using a trained model to perform prediction on a verification set and verifying during practical business. The Electricity fee sensitive user analysis method based on stacking and bagging algorithms can improve mastery degree of an electric power company about electricity fee sensitivity of the user, and is beneficial for providing differential and specific quality power supply service.

Description

technical field [0001] The invention relates to the technical field of electric power, and more specifically, relates to a method for analyzing electric charge-sensitive users based on stacking and bagging algorithms. Background technique [0002] With the deepening of the reform of the power system and the gradual release of the power sales side to social capital, the current extensive operation and unified customer service content and model are difficult to meet the growing requirements for personalized and precise customer service experience . How to make full use of existing data resources, dig deep into the potential needs of customers, improve the quality of power supply services, and enhance customer stickiness are crucial to the company's future development. Customers with strong sensitivity to power services often have higher requirements for the quality and methods of services such as electricity bill metering, power supply quality, and power marketing, and have b...

AI Technical Summary

Problems solved by technology

[0004] The inventor found in the process of realizing the present invention that the degree of analysis and utilization of these data by the power company is not high enough

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