Research on the Self-Repairing Model of Outliers in Energy Data Based on Regional Convergence

The need for the statistical stability of data is increasing nowadays as the data resource has become a more and more important production factor. In this study, a set of general identification and correction models are established for data outlier modification. The research object we chose is the d...

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Bibliographic Details
Published inEnergies (Basel) Vol. 13; no. 18; p. 4909
Main Authors Li, Nan, Zhao, Xunwen, Mu, Hailin, Li, Yimeng, Pang, Jingru, Jiang, Yuqing, Jin, Xin, Pei, Zhenwei
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.09.2020
Subjects
Big Data
club convergence
Clubs
Convergence
Economic growth
Energy consumption
Energy industry
Growth models
half-life cycle
Hypotheses
Income distribution
Labor productivity
Lagrange multiplier
Manufacturing
outliers
Outliers (statistics)
Per capita
Regions
Time series
United States > US
Peru
China
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Summary:The need for the statistical stability of data is increasing nowadays as the data resource has become a more and more important production factor. In this study, a set of general identification and correction models are established for data outlier modification. The research object we chose is the data of per capita energy consumption. Based on the joint diagnosis method of outliers and the regional convergence theory, the abrupt outliers are identified and corrected. The study finds that there is an outlier in the data of the Ningxia Hui Autonomous Region. According to the club grouping method, 30 provinces in China are divided into two clubs and the Ningxia Hui Autonomous Region is determined to be in the first club. We calculate the convergence rate and obtain the correction results combining the half-life cycle model.
ISSN:1996-1073
1996-1073
DOI:10.3390/en13184909