Electricity Load Profile Characterisation for Industrial Users Based On Normal Cloud Model and iCFSFDP Algorithm

Abstract

Electricity load profile characterisation of industrial users is fundamentally essential for user-side load management and demand response by extracting electricity consumption patterns, which requires load feature-based similarity measurement and accurate curve clustering. Given this background, a typical load curve identification method based on the normal cloud model and the improved clustering by fast search and find of density peaks (iCFSFDP) algorithm is proposed for electricity load profile characterisation of industrial users. First, a piecewise cloud approximation (PWCA) based load dynamic feature extraction algorithm is proposed to establish the piecewise cloud models of daily load curves based on the normal cloud theory, and the overlapping area between clouds is defined to measure the similarity between two load curves for clustering. Second, the iCFSFDP based load curve clustering algorithm is proposed to improve the clustering accuracy by implementing the hierarchical aggregation process. Considering the dispersion and massiveness of metered data in the electricity consumption of industries, a distributed-centralized identification method that extracts the typical curves of each user and the whole industry in a distributed and centralized way respectively is proposed to improve the computational efficiency and the clustering effectiveness. Finally, case studies on the industrial users in Zhejiang province, China show that the proposed method can measure the piecewise power consumption features among load curves comprehensively and determine load curve clusters corresponding to the cloud distance-based similarities, thus helping identify more accurate typical load curves that characterize different electricity consumption profiles.