Missing Data estimation with a bi-dimensional adaptive weighted method for power grid data. Issue 3 (April 2019)

• Record Type: Journal Article
• Authors: Wang, Xi; Li, Mingwei; Zhou, Jianjia; Deng, Yan; Zhang, Qingqing
• Is Part Of: IOP conference series. Volume 490:Issue 3(2019)
• Publication Date: 2019-04
• Subjects: Materials science -- Periodicals; 620.1105
• DOI: 10.1088/1757-899X/490/4/042025
• Languages: English
• ISSNs: 1757-8981

Abstract

Abstract: The power grid data often contain missing values, which will seriously affect the analysis of the power grid development trend. To estimate missing values, the k-nearest neighbor and linear regression imputation method have been proved to be effective. These methods only consider the correlation between the samples or just consider the correlation between the features. However, we do not know which correlation of the two dimensions is more important in some special missing values. So this paper proposes a bi-dimensional adaptive weighted imputation method which considers the correlation of the two directions simultaneously. The experimental results show that the bi-dimensional adaptive weighted imputation method can reduce the estimation error.