Open AccessAN OUTLIER DETECTION METHOD OF LOW-VOLTAGE USERS BASED ON WEEKLY ELECTRICITY CONSUMPTION
Author(s) -
Tang Xiao-feng,
Rui Huang,
Qi Chen,
Zhaoyi Peng,
Hao Wang,
Wang Bi-heng
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/631/4/042004
Subject(s) - electricity , consumption (sociology) , cluster analysis , voltage , stand alone power system , power consumption , computer science , local outlier factor , power (physics) , electricity generation , engineering , electrical engineering , artificial intelligence , social science , physics , quantum mechanics , sociology
A method of detecting abnormal electricity consumption behavior of low-voltage electricity consumption users based on weekly electricity consumption is proposed. Firstly, features of weekly electricity consumption curve data are extracted from the actual daily consumption data. Fuzzy C-means clustering algorithm is used to classify electricity consumption behavior. Typical weekly electricity consumption characteristic curves of low-voltage power users are obtained, and then low-voltage users with abnormal electricity consumption are marked based on distance. The effectiveness and feasibility of the algorithm are verified by the actual electricity consumption data of 15 stations in Yang-Zhou City.