Classification Analysis Method for Residential Electricity Consumption Behavior Based on Recurrence Plot (RP) and Convolutional Auto-Encoder (CAE). Issue 1 (January 2021)

• Record Type: Journal Article
• Authors: Sun, Zhiqing; Deng, Xinyu; Xu, Xianghai; Hou, Weihong; Wang, Shouxiang
• Is Part Of: IOP conference series. Volume 645:Issue 1(2021)
• Publication Date: 2021-01
• Subjects: Earth sciences -- Periodicals; Environmental sciences -- Congresses; Environmental sciences -- Periodicals; 550.5
• DOI: 10.1088/1755-1315/645/1/012075
• Languages: English
• ISSNs: 1755-1307

Abstract

Abstract: Load clustering is the foundation of big data mining in power distribution system. It is helpful for power companies to accurately grasp users' electricity consumption habits, improve power quality and develop demand response. To overcome the characteristic redundancy problem of the high-dimensional load data, the load clustering method based on RP and CAE is proposed. Firstly, the one-dimensional load curves are converted into two-dimensional recurrence plot to realize feature enhancement. Secondly, the advanced feature extraction capability of CAE is used to realize load feature extraction and dimension reduction. Finally, the spectrum clustering (SC) is used to analyze the user's electricity consumption patterns. The validity of proposed method is verified by Ireland Smart meter dataset.