DocumentCode
779779
Title
Emergent electricity customer classification
Author
Chicco, G. ; Napoli, R. ; Piglione, F. ; Postolache, P. ; Scutariu, M. ; Toader, C.
Author_Institution
Dipt. di Ingegneria Elettrica Ind., Torino, Italy
Volume
152
Issue
2
fYear
2005
fDate
3/4/2005 12:00:00 AM
Firstpage
164
Lastpage
172
Abstract
Various techniques for electricity customer classification are presented and discussed, with the focus on highlighting the behaviour of electricity customers. The surveyed techniques include classical approaches (applications of statistics and deterministic clustering algorithms), as well as methods based on artificial intelligence (neural networks and fuzzy systems). The classification techniques are illustrated by using various sets of features characterising the shape of the load patterns. Different approaches for feature selection, both in the time and in the frequency domain, are discussed. A number of specific metrics, some of which were originally developed by the authors, are applied in order to quantify the classification adequacy and to identify the most suitable classification techniques. Detailed results obtained from real life applications are provided.
Keywords
classification; customer profiles; deterministic algorithms; fuzzy systems; neural nets; pattern clustering; power engineering computing; statistical analysis; artificial intelligence; classification statistics; deterministic clustering algorithms; electricity customer behaviour; electricity customer classification; fuzzy systems; neural networks;
fLanguage
English
Journal_Title
Generation, Transmission and Distribution, IEE Proceedings-
Publisher
iet
ISSN
1350-2360
Type
jour
DOI
10.1049/ip-gtd:20041243
Filename
1421133
Link To Document