期刊
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
卷 10, 期 2, 页码 1327-1336出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2014.2311968
关键词
Data mining; knowledge discovery; time series analysis
类别
资金
- U.K. Technology Strategy Board [100923, TP 3981-33147]
This work presents data mining methods for discovering unusual consumption patterns and their associated descriptive models from smart electricity meter data. At present, data mining and knowledge discovery in electricity meter data suffer from three notable weaknesses: 1) insufficient focus on intelligent data analysis of subgroups (subsets) whose patterns vary significantly from aggregate patterns embodied in an entire dataset; 2) a lack of effort towards generating intuitively understandable and practically applicable knowledge for industrial practitioners to identify such subgroups; and 3) limited knowledge regarding the link between unusual consumption patterns and household consumers' socio-demographic characteristics. This paper addresses these practically important but technically challenging issues by applying subgroup discovery algorithms to a real smart electricity meter dataset. Subgroups whose patterns are unusual and whose sizes are large enough are discovered, and their descriptive and predictive models are generated. Furthermore, to enrich subgroup discovery algorithms, three new-quality measures for real-valued targets are proposed. The comparative studies empirically evaluate the effectiveness and usefulness of subgroup discovery on classification accuracy, predictive power, and computational resources. The methodologies and algorithms presented are generic, and therefore applicable to a wider range of data mining problems.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据