Application of Fuzzy Clustering to Determine Electricity Consumers' Load Profiles

In a regulated environment, load profiles have been employed to provide information for forecasting, system planning and demand side planning. However, in the deregulated environment, consumers can purchase electricity from any provider regardless of size and location. As a result, load profiles have become more significant. The determination of customer load profile may facilitate utility companies with better marketing strategies and improved efficiency in operating the current facilities. This paper examined the capability of fuzzy clustering to determine consumers load profiles on the basis of their electricity behaviour. Two techniques in fuzzy clustering namely, fuzzy relation and fuzzy c-means (FCM) were employed in this work. The load data used in this work are from actual measurements from different feeders derived from a distribution network. Cluster validity indices will be used to determine the optimum clusters. The performance of each algorithm will be evaluated by employing adequacy indices i.e. mean index adequacy (MIA) and clustering dispersion indicator (GDI).