Default electricity risk model characteristic selection method and device and equipment

Abstract

The embodiment of the invention discloses a default electricity risk model characteristic selection method and device and equipment. The method comprises the steps that S101 the default electricity tag of a user and the characteristic factor of the user are acquired; S102 an LASSO penalty function is constructed according to the default electricity tag and the characteristic factor; S103 the LASSO penalty function is solved through modified LARS to acquire the valid set of arguments of the LASSO penalty function; and S104 the arguments are filtered according to a set filtering rule and the valid set, so as to acquire a selected characteristic factor. According to the technical scheme provided by the embodiment of the invention, the selection efficiency and the effectiveness of the characteristic factor are improved.

Description

technical field [0001] The embodiments of the present invention relate to a feature factor selection method, and in particular to a feature selection method, device and equipment for a power default risk model. Background technique [0002] The customer information of electric power enterprises involves massive data such as real-time power data of the metering automation system, GIS (Geographic Information System, geographic information system) data, power grid trend information, and 95598 customer service recordings. These data come from a wide range of sources, including internal data and external data, and there are many types of data. With the improvement of the informatization level of power grid enterprises, the in-depth development of mobile Internet and big data technology, the data related to customers has shown explosive growth. As technology advances. On the one hand, the application of electric power big data is to integrate information such as macroeconomics, p...

AI Technical Summary

Problems solved by technology

This may lead to problems such as the selection of independent variables that have little or no influence on the dependent variable, and the collinearity of independent variables, which will lead to an increase in the amount of calculation in the modeling process

On the other hand, the current number of users who have breached the contract in history extracted by the system is too small. Using this as a sample, the accuracy of the prediction model established is not high, which also leads to a decrease in the accuracy of the final estimation and prediction.

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