A Data Mining Based NTL Analysis Method

Author

Nizar, A.H. ; Dong, Z.Y. ; Zhao, J.H. ; Zhang, P.

Author_Institution

Sch. of Inf. Technol. & Electr. Eng., Univ. of Queensland, St. Lucia, QLD

fYear

2007

fDate

24-28 June 2007

Firstpage

1

Lastpage

8

Abstract

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, Naive 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.

Keywords

belief networks; classification; data mining; decision trees; NTL analysis; Naive Bayesian; consumption behaviour; data mining; decision tree; electricity distribution sector; historical data; maximum accuracy; nontechnical loss analysis; popular classification algorithms; Australia; Bayesian methods; Classification algorithms; Classification tree analysis; Data mining; Decision trees; Energy loss; Information technology; Propagation losses; Testing; Classification Algorithms; Customer Behaviour; Data Mining; Decision Tree; Naive Bayesian; Non-Technical Loss (NTL);

fLanguage

English

Publisher

ieee

Conference_Titel

Power Engineering Society General Meeting, 2007. IEEE

Conference_Location

Tampa, FL

ISSN

1932-5517

Print_ISBN

1-4244-1296-X

Electronic_ISBN

1932-5517

Type

conf

DOI

10.1109/PES.2007.385883

Filename

4275649