loading...
A Method for Classification of Electricity Demands Using Load Profile Data
Jeju Island, South Korea July 14-July 16
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICIS.2005.11Fourth Annual ACIS International Conf ...
 This Article 
 
Buy PURCHASE ARTICLE: $19
PDF
HTML
Buy IEEE Xplore Subscribers
 
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
ASCII Text
x 
 
In Hyeob Yu, Jin Ki Lee, Jong Min Ko, Sun Ic Kim, "A Method for Classification of Electricity Demands Using Load Profile Data," Computer and Information Science, ACIS International Conference on, pp. 164-168, Fourth Annual ACIS International Conference on Computer and Information Science (ICIS'05), 2005.
 
 
BibTex
x
 
@article{ 10.1109/ICIS.2005.11,
author = {In Hyeob Yu and Jin Ki Lee and Jong Min Ko and Sun Ic Kim},
title = {A Method for Classification of Electricity Demands Using Load Profile Data},
journal ={Computer and Information Science, ACIS International Conference on},
volume = {0},
year = {2005},
isbn = {0-7695-2296-3},
pages = {164-168},
doi = {http://doi.ieeecomputersociety.org/10.1109/ICIS.2005.11},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Computer and Information Science, ACIS International Conference on
TI - A Method for Classification of Electricity Demands Using Load Profile Data
SN - 0-7695-2296-3
SP164
EP168
A1 - In Hyeob Yu,
A1 - Jin Ki Lee,
A1 - Jong Min Ko,
A1 - Sun Ic Kim,
PY - 2005
KW - null
VL - 0
JA - Computer and Information Science, ACIS International Conference on
ER -
 
In Hyeob Yu, Korea Electric Power Research Institute
Jin Ki Lee, Korea Electric Power Research Institute
Jong Min Ko, Korea Electric Power Research Institute
Sun Ic Kim, Korea Electric Power Research Institute
The multiple participants of the electricity market need new business strategies for providing value added services to customer. Therefore they need the accurate customer information about the electricity demand. Demand characteristic is the most important one for analyzing customer information. In this study load profile data, which can be collected through the Automatic Meter Reading System, are analyzed for getting demand patterns of customer. The load profile data include electricity demand in 15 minutes interval. An algorithm for clustering similar patterns is developed using the load profile data. As results of classification, representative curves for the same groups are generated. The demand characteristics of the groups are discussed. Also, the compositions of demand contract and industrial classification in each group are presented.
Citation:
In Hyeob Yu, Jin Ki Lee, Jong Min Ko, Sun Ic Kim, "A Method for Classification of Electricity Demands Using Load Profile Data," icis, pp.164-168, Fourth Annual ACIS International Conference on Computer and Information Science (ICIS'05), 2005
Usage of this product signifies your acceptance of the Terms of Use.


Suggestions