Consumer Load Profiling using Fuzzy Clustering and Statistical Approach

Load profiling present useful tool for onitoring typical load shape for a group of customers, which can be performed from past or current day data. In a deregulated energy environment, consumers can purchase electricity from any provider regardless of size and location. As a result, there is a growing interest in understanding the nature of variations in consumer´s consumption. This information can be used to facilitate electricity supplier in their marketing strategy. Many techniques for load profiling have been reported in the past. The techniques include applications of statistics, unsupervised clustering technique and methods based on frequency domain approach. This paper compares the application of fuzzy clustering with statistical method in load profiling. K-means has been chosen as the statistical approach employed in this study. These two approaches have the same objectives i.e. to recognise similarities, clusters and classify the individual load profiles of different customers to one of the identified categories. The paper evaluates the performance of each method and discusses the strength and weaknesses of both approaches based on the simulated results.