Clustering of electricity demand to generate virtual load profile / Marliah Mostakim

• Main Author: Mostakim, Marliah
• Format: Thesis
• Language: English
• Published: 2012
• Online Access: https://ir.uitm.edu.my/id/eprint/84625/1/84625.pdf; https://ir.uitm.edu.my/id/eprint/84625/

Recently the emerging issue in the electric industry is effective power based on Smart Grid. To operate the power effectively, the data must be applicable and accessible, thus will produce the virtual load profile (VLP). To generate VLP clustering and classification are required. The clustering of customers electricity demand becomes important not only to design tariff but also to identify sets of standard load profile. Electricity demand means the maximum amount of electricity is being used at some time while the load profile can refer to a number of different forms of data. Clustering is one of the methods that can be used to perform the data. Clustering represent groups of customers with the same clusters are very similar and the different clusters become very distinct. In this paper, focus is on K-means and Hierarchical for clustering electricity demand and their differences are analyzed.