Open AccessResearch on Power Behavior Analysis Based on Clustering
Author(s) -
Xunjia Li,
Tao Zhang
Publication year - 2019
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/300/4/042033
Subject(s) - cluster analysis , fuzzy clustering , cure data clustering algorithm , data mining , data stream clustering , correlation clustering , computer science , canopy clustering algorithm , preprocessor , k medians clustering , artificial intelligence
This research is based on data clustering to analyze power users’ electricity consumption behavior, and analyzes the load curve to obtain the users’ electricity consumption characteristics and classify users according to the characteristics of electricity consumption behavior. It is of far-reaching significance to the power industry and socio-economic development. This paper introduces the principle and flow of the main clustering algorithm K-means, fuzzy clustering and neural network clustering algorithm. The data preprocessing method is given. The clustering algorithm and the optimal clustering number are determined by defining the clustering volatility and clustering accuracy index. Then the power consumption behavior of the user is analyzed through the load curves and characteristic parameters obtained through clustering.