TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
Power Failure Sensitivity Analysis via Grouped 𝐿1/2 Sparsity Constrained Logistic Regression |
| 영문제목(English Title) |
Power Failure Sensitivity Analysis via Grouped 𝐿1/2 Sparsity Constrained Logistic Regression |
| 저자(Author) |
Baoshu Li
Xin Zhou
Ping Dong
|
| 원문수록처(Citation) |
VOL 15 NO. 08 PP. 3086 ~ 3101 (2021. 08) |
한글내용
(Korean Abstract) |
|
영문내용
(English Abstract) |
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 𝑙1/2 sparsity constrained logistic regression method for sensitivity assessment of electric power failure. Different from the 𝑙𝑙1 norm and k-support norm, the proposed grouped 𝑙1/2 sparsity constrained logistic regression method simultaneously imposes the inter-class information and tighter approximation to the nonconvex 𝑙0 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, 𝑙1/2 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. |
| 키워드(Keyword) |
Power Failure Sensitivity Analysis
L1/2 Sparsity
Logistic Regression
Alternating Directions of Multipliers
|
| 파일첨부 |
PDF 다운로드
|
