Power Failure Sensitivity Analysis via Grouped ?1/2 Sparsity Constrained Logistic Regression

To supply precise marketing and differentiated service for the electric power service department, it is very important to predict the customers with high sensitivity of electric power failure. To solve this problem, we propose a novel grouped ll1/2 sparsity constrained logistic regression method for sensitivity assessment of electric power failure. Different from the ll1 norm and k-support norm, the proposed grouped ll1/2 sparsity constrained logistic regression method simultaneously imposes the inter-class information and tighter approximation to the nonconvex ll0 sparsity to exploit multiple correlated attributions for prediction. Firstly, the attributes or factors for predicting the customer sensitivity of power failure are selected from customer sheets, such as customer information, electric consuming information, electrical bill, 95598 work sheet, power failure events, etc. Secondly, all these samples with attributes are clustered into several categories, and samples in the same category are assumed to be sharing similar properties. Then, ll1/2 norm constrained logistic regression model is built to predict the customer's sensitivity of power failure. Alternating direction of multipliers (ADMM) algorithm is finally employed to solve the problem by splitting it into several sub-problems effectively. Experimental results on power electrical dataset with about one million customer data from a province validate that the proposed method has a good prediction accuracy.

• Publication year

  2021

• Venue

  KSII Transactions on Internet and Information Systems

• Publication date

  2021-08-31

• Fields of study

  Computer Science, Engineering

• Identifiers
  DOI 10.3837/tiis.2021.08.021
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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