DocumentCode :
41135
Title :
Household Energy Consumption Segmentation Using Hourly Data
Author :
Jungsuk Kwac ; Flora, June ; Rajagopal, Ram
Author_Institution :
Dept. of Electr. Eng., Stanford Univ., Stanford, CA, USA
Volume :
5
Issue :
1
fYear :
2014
fDate :
Jan. 2014
Firstpage :
420
Lastpage :
430
Abstract :
The increasing US deployment of residential advanced metering infrastructure (AMI) has made hourly energy consumption data widely available. Using CA smart meter data, we investigate a household electricity segmentation methodology that uses an encoding system with a pre-processed load shape dictionary. Structured approaches using features derived from the encoded data drive five sample program and policy relevant energy lifestyle segmentation strategies. We also ensure that the methodologies developed scale to large data sets.
Keywords :
demand side management; power consumption; smart meters; AMI; CA smart meter data; encoding system; energy lifestyle segmentation strategies; hourly data; household electricity segmentation methodology; household energy consumption segmentation; pre-processed load shape dictionary; residential advanced metering infrastructure; Clustering algorithms; Dictionaries; Electricity; Encoding; Shape; Sociology; Statistics; Clustering; demand response; segmentation; smart meter data; variability;
fLanguage :
English
Journal_Title :
Smart Grid, IEEE Transactions on
Publisher :
ieee
ISSN :
1949-3053
Type :
jour
DOI :
10.1109/TSG.2013.2278477
Filename :
6693793
Link To Document :
بازگشت