A Moving Shape-based Robust Fuzzy K-modes Clustering Algorithm for Electricity Profiles

Abstract Clustering algorithms have been proven to be an effective method to identify representative energy consumption patterns, as well as being a pre-processing step for other applications (such as demand response, load prediction). This paper proposes a novel moving shape-based robust fuzzy K-modes (MS-RFKM) clustering method, aiming to accurately identify shape patterns in time-series sequences. Specifically, a novel distance measurement-shape feature matrix (SFM) is proposed, which is directly derived from the original load profiles and can accurately depict the shape features of load profiles. Besides, SFM helps to reduce the computation complexity and decrease the adverse impact of noise/ amplitude distortion. Meanwhile, the number of clusters is optimally determined by integrating moving procedure of hierarchical algorithm into the proposed shape-based robust fuzzy K-modes (S-RFKM) method. And the optimal centroids of clusters can be optimally fixed by dynamic time warping (DTW) based fuzzy K-modes (D-FKM). The presented algorithm is validated using users’ metering data from China. The simulation results demonstrate that the proposed method can better capture the energy usage patterns and improve the clustering stability and robustness, compared with conventional clustering methods.