NORHASNELLY ANUAR et al: CLUSTER VALIDITY ANALYSIS AND OPTIMIZATION OF FUZZIFICATION

Information from load profile is useful for electricity suppliers to plan their generation, improving their market strategies and load balancing. Consumers in the new liberalized market have the opportunity of choosing their electricity suppliers between several suppliers and the possibility to access to new products and services from them. Hence they need the knowledge of load profile to help them choose their electricity supplier. On the suppliers’ side, power market becomes competitive and energy commercializers are becoming more interested in the development of new suitable strategies and products to be offered to each of their different user or to find new market opportunities. A lot of efforts have been made to investigate methodologies to form optimal efficiency in determining typical load profiles (TLPs), derived from various clustering and classification techniques. Methodologies proposed in previous work have disadvantages such as time consuming, expensive, poor performance over large scale simulation and produced overlapped data in the obtained TLPs. This project proposes a methodology for determining consumers’ TLPs by using Fuzzy C-means (FCM) clustering method. Initial determination of the number of clusters and fuzzification parameters in FCM greatly influence the resulting clusters. Hence the optimal number of cluster for FCM is obtained through cluster validity analysis and the best value of fuzzification parameter, m of FCM is determined to ensure the optimal result of FCM is obtained.