Abstract: The prediction of customers with high sensitivity of electric power failure can provide data and decision support for the electric power service departments to offer precision marketing and differentiated services. With regard to the electric power failure sensitivity problem, we propose the electric power failure sensitivity assessment algorithm using ksupport norm regularized logistic regression. Different from the normal l1 norm, ksupport norm is the tighter convex relaxation of l0 norm on the Euclidean norm unit ball and able to select multiple correlated variables to predict the response, which can promote the accuracy of predicted results. Firstly, the variables or factors for predicting response are selected from multiple aspects including the customer information, electric consuming information, electrical bill information, 95598 work sheet, power failure events, etc. The sample set is constructed by collecting the variable information of each consumer. Secondly, ksupport norm regularized logistic regression model is used to predict customers with high sensitivity of electric failure. In terms of forwardbackward operator splitting, an iterative optimization algorithm is also proposed to decompose the original problem into two subproblems and solve the model effectively. Furthermore, dominance analysis method is adopted to identify the importance of each variable for predicting the response result. The model is validated by using about one million customer data from a province supply board and has good prediction accuracy. The experimental results demonstrate the effectiveness of our prediction model.

Key words:  electric power failure sensitivity, logistic regression, ksupport sparse, dominance analysis, operator splitting