期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2016
卷号：92
期号：1
出版社：Journal of Theoretical and Applied
摘要：Feature selection is the essential process to obtain the best feature vectors in pattern recognition system. These feature vectors contain information describing the original data�s important characteristics. In this research, a framework based on factor analysis technique namely the Principal Component Analysis (PCA) is performed to determine the best features extracted from the daily load curve prior to clustering process. The rules of thumb applied include Bartlett�s test of sphericity, Kaiser-Meyer-Olkin (KMO) measure, Kaiser Criterion, Scree test along with Varimax approach. Accordingly, KMO as well as Bartlett�s test suggested the data factorability is significant. Furthermore, Kaiser Criterion and Scree test together with component matrix approach implied that the first two most significant factor must be retained whilst Varimax approach confirmed that clustering analysis should comprise of the entire load curve values. Upon selection of features, the capability of fuzzy clustering in classifying these features attained from 247 feeders in a particular distribution network is examined. Initial results demonstrated the effectiveness of feature selection process and the potential of fuzzy clustering in particular the fuzzy c- means (FCM) in classifying electrical energy consumption.
关键词：Feature Selection; Load profiling; clustering; fuzzy relation; Principal Component Analysis
