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	Kernel-Based Non-parametric Clustering for Load Profiling of Big Smart Meter Data
	Pan, Erte ; Li, Husheng ; Song, Lingyang ; Han, Zhu
	2015
关键词	big data smart meters kernel PCA non-parametric clustering mixture models gap statistic
英文摘要	The emergence of smart meters has enabled the new energy efficiency services in an automatic fashion. With the information and communication technology, the smart meters are devised to gather and communicate the information of electricity suppliers and residential electricity consumers to ameliorate the efficiency of power distribution as well as the sustainability of the power resources. Due to the enormous amount of electricity consumers, the analysis of the big data produced by the smart meters is a crucial challenge faced by the electricity companies and researchers. In this paper, we analyze the big data based on the smart meter readings collected in the Houston area. The statistical properties of the data is investigated such that the behaviors of the consumers can be better understood. Moreover, the kernel PCA analysis and non-parametric clustering of the data gives a comprehensive guidance on what are the potential clusters of the customers and how to allocate the power more efficiently.; CPCI-S(ISTP); epan@uh.edu; husheng@eecs.utk.edu; lingyang.song@pku.edu.cn; zhan2@uh.edu; 2251-2255
语种	英语
出处	IEEE Wireless Communications and Networking Conference (WCNC)
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/450135]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Pan, Erte,Li, Husheng,Song, Lingyang,et al. Kernel-Based Non-parametric Clustering for Load Profiling of Big Smart Meter Data. 2015-01-01.

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