Title :
Clustering of smart meter data for disaggregation
Author :
Ford, Vitaly ; Siraj, Ambareen
Author_Institution :
Comput. Sci. Dept., Tennessee Technol. Univ., Cookeville, TN, USA
Abstract :
This research addresses privacy concerns in smart meter data. Smart meter data is analyzed for learning normal consumer usage of electricity. Clustering technique such as Fuzzy C-Means is used to disaggregate and learn energy consumption patterns in smart meter data. Results of experimentation with real world meter data demonstrate that it is realistically possible to profile the electricity consumption behavior of consumers analyzing their usage captured by smart meters.
Keywords :
consumer electronics; data privacy; pattern clustering; power consumption; power engineering computing; smart meters; consumer electricity consumption behavior; consumer electricity usage; energy consumption pattern learning; smart meter data clustering; smart meter data privacy; Clustering algorithms; Computer security; Data privacy; Energy consumption; Indexes; Smart grids; Disaggregation; Fuzzy C-Means Clustering; Security; Smart Meter;
Conference_Titel :
Global Conference on Signal and Information Processing (GlobalSIP), 2013 IEEE
Conference_Location :
Austin, TX
DOI :
10.1109/GlobalSIP.2013.6736926
