Two-stage fuzzy clustering approach for load profiling

The electricity industries need new business strategies for providing value added service to consumer. Analysis of consumer behavior in handling load usage is vital since it could provide the demand characteristic of a particular consumer. This information could be used in making new marketing strategies, electricity pricing and policy making. A demand characteristic could be extracted from the monthly billing data but sometimes it is insufficient. Besides that, another technique that could be applied is by installing time interval meter at each of the point's demands. However, this method is expensive and costly due to equipment, maintenance and processing cost. Therefore, load profile acquired by classifying the load curves is seen as one of cost-effective approach in analyzing the consumers' demand characteristics. This paper investigates the capability of two-stage fuzzy clustering in classifying electricity daily load curve from different feeders in a particular distribution network. Two stage fuzzy clustering based on fuzzy c-means (FCM) has been employed in this work. In addition, a factor analysis technique namely Principal Component Analysis (PCA) is used to examine the daily load curve in determining the most valuable features of the load data prior to clustering process. Results obtained demonstrate the ability of the proposed method in classifying electricity demands according to the energy consumption.