A data mining based NTL analysis method

Nizar, A. H., Dong, Z. Y., Zhao, J. and Zhang, P. (2007). A data mining based NTL analysis method. In: Kirshen, D., IEEE Power Engineering Society General Meeting. 2007 IEEE Power Engineering Society General Meeting, Tampa, Florida, USA, (4275649.1-4275649.8). 24-28 June 2007. doi:10.1109/PES.2007.385883


Author Nizar, A. H.
Dong, Z. Y.
Zhao, J.
Zhang, P.
Title of paper A data mining based NTL analysis method
Conference name 2007 IEEE Power Engineering Society General Meeting
Conference location Tampa, Florida, USA
Conference dates 24-28 June 2007
Proceedings title IEEE Power Engineering Society General Meeting
Journal name 2007 Ieee Power Engineering Society General Meeting, Vols 1-10
Place of Publication Piscataway, NJ, United States
Publisher IEEE
Publication Year 2007
Sub-type Fully published paper
DOI 10.1109/PES.2007.385883
ISBN 1424412986
9781424412983
ISSN 1932-5517
Editor Kirshen, D.
Start page 4275649.1
End page 4275649.8
Total pages 8
Collection year 2008
Language eng
Abstract/Summary This paper presents a method of determining which type of data provides maximum accuracy with reference to non-technical loss analysis in the electricity distribution sector. The method is based on two popular classification algorithms, Naïve Bayesian and Decision Tree. It involves extracting the patterns of customers’ kWh consumption behaviour from historical data and arranging the data in various ways by averaging them yearly, monthly, weekly, and daily. Both techniques are used and compared. The intention is to ensure the acquisition of optimum results in developing representative load profiles to be used as the reference for non-technical loss analysis directed at detecting any significant activities that may contribute to such losses.
Subjects 299999 Engineering and Technology not elsewhere classified
660000 - Energy Supply
E1
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ

 
Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in Thomson Reuters Web of Science Article
Scopus Citation Count Cited 0 times in Scopus Article
Google Scholar Search Google Scholar
Access Statistics: 83 Abstract Views  -  Detailed Statistics
Created: Mon, 21 Apr 2008, 09:49:09 EST by Donna Clark on behalf of School of Information Technol and Elec Engineering