With the rapid development of the smart grid, a large volume of smart meter data are collected in the form of time series, which is called load profiles. This paper investigates the load profile clustering of smart grid customers, which is significant for many applications. An adaptive weighted fuzzy clustering algorithm is proposed to cluster load profiles, where Principle Component Analysis (PCA) is used to reduce the data dimension, and then weighted Fuzzy C-Means (FCM) is adopted to cluster the big data. The optimal number of clusters is determined adaptively by integrating a clustering validity function into the clustering algorithm. The simulation results show that the proposed algorithm could achieve considerable improvement both in time complexity and clustering accuracy through comparing four clustering validity indexes.
['Yun Lu', 'Tiankui Zhang', 'Zhimin Zeng']
Adaptive weighted fuzzy clustering algorithm for load profiling of smart grid customers