Analysis of user electricity consumption behavior based on density peak clustering with shared neighbors and attractiveness. (17th November 2022)

• Record Type: Journal Article
• Authors: Li, Qingpeng; Wang, Gang; Zhang, Yang; Yang, Qin
• Is Part Of: Concurrency and computation. Volume 35:Number 3(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-11-17
• Subjects: analysis of electricity consumption -- attractiveness -- density peak clustering -- electricity big data -- weighted shared neighbors; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.7518
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: User behavior analysis is the research foundation of power load forecasting and power abnormal detection, and it is the theoretical support for smart grid planning and the construction of energy internet. Aiming at the complex characteristics of high‐dimensional, noisy, and multi‐redundant of power load data, this article used the principal component analysis (PCA) to reduce the dimensionality of power data. The density peaks clustering algorithm with shared neighbor and attractiveness (DPC‐SNA) was then used to cluster the data with reduced dimensionality to extract the user's electricity consumption characteristics. The DPC‐SNA algorithm first constructs a sample similarity measurement criterion that shares the similarity of neighbors, on which the local density is defined accordingly. The new local density can effectively distinguish the contributions of local samples and global samples. Incorporating the idea of universal gravitation, a new calculation method of sample attractiveness was defined, and the remaining samples were allocated by the attractiveness matrix. Experiment was performed using the actual load data of special transformer users in a certain area. The results show that there were five typical electricity consumption behavior characteristics in this area, namely, the evening‐peak production type, all‐day production type, multi‐peak production type, daytime production type, and night production type. The corresponding load peak time periods were also obtained.