Electric charge sensitivity assessment method based on logistic regression

Abstract

The invention discloses an electric charge sensitivity assessment method based on logistic regression. According to the method, electric charge sensitivity models are respectively established for high-voltage users, low-voltage non-resident customers, and resident customers by regarding customer sensitivity as the entry point. The main steps comprise: collecting modeling indexes from a plurality of dimensions including customer basic information, electricity consumption information, and payment information etc., screening variables by employing information values (IV) and related coefficients, grouping the variables based on the optimal grouping algorithm and the optimal clustering algorithm, performing conversion of weight of evidence (WOE), establishing the customer electric charge sensitivity assessment model by employing a logistic regression algorithm, constructing a standard scoring card which is easily understood and implemented according to model parameter estimation values, and finally determining the weights of the variables through an advantage analysis method. According to the method, data support is provided for development of accurate marketing and differentiated service by departments of electric power marketing and customer service through recognition of customers with high electric charge sensitivity, the overall satisfaction degree of the customers is improved, and the customer perception is improved.

Description

technical field [0001] The invention relates to the field of electric power, in particular to a method for evaluating sensitivity of electricity charges based on logistic regression. Background technique [0002] Power grid companies have large customers and complex production and operation conditions. For a long time, it has faced great pressure in terms of customer service and emergency repairs. The complaint rate is relatively high among State Grid companies, and customer satisfaction is not high. In order to improve service quality and improve operation and maintenance efficiency, based on big data analysis and mining technology, from the complex data, find out the influencing factors of customer complaints, consultations and other behaviors, and take preventive measures and service preparations in advance to improve work quality and service levels. [0003] In current business practice, the models used to carry out forecasting mainly include logistic regression, decis...

AI Technical Summary

Problems solved by technology

[0004] In the existing logistic regression process, after the data is processed, users cannot see the results intuitively, that is, they cannot accurately identify customers with high sensitivity to electricity bills

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