Determination of fuzziness parameter in load profiling via Fuzzy C-Means

Load profiling has become an important issue in power industry and has gain more attention from utility company worldwide due to deregulation and liberalization. A lot of work had been done to obtain a method to determine typical load profiles (TLPs) of electricity consumers. Load profiles represents consumers electricity consumption pattern and provide useful data to both consumer and electricity provider. This paper presents the TLPs determination through clustering technique by using Fuzzy C-Means (FCM) algorithm. Two of the most important parameters in FCM are fuzziness parameter, m and optimal number of cluster, c. This paper shows the determination of the suitable fuzziness parameter through observation of experimental result of the cluster validity indexes value. Cluster validity indexes were used to determine c. Three cluster validity indexes were discussed in this paper. They are Xie-Beni index, Non-fuzzy index and Davies-Bouldin index. Objectives of this paper are to obtain groups of TLPs by using FCM clustering and to determine the suitable value of the fuzziness parameter, m. The data used in this project are obtained from Tenaga Nasional Berhad (TNB).