Slope-Based Shape Cluster Method for Smart Metering Load Profiles

Cluster analysis is used to study the group of load profiles from smart meters to improve the operability in distribution network. The traditional K-means clustering analysis method employs Euclidean distance as similarity measurement, which is insufficient in reflecting the shape similarities of lo...

Full description

Saved in:
Bibliographic Details
Published inIEEE transactions on smart grid Vol. 11; no. 2; pp. 1809 - 1811
Main Authors Xiang, Yue, Hong, Juhua, Yang, Zhiyu, Wang, Yang, Huang, Yuan, Zhang, Xin, Chai, Yanxin, Yao, Haotian
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 01.03.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Algorithms
Cluster analysis
Clustering
Clustering algorithms
Energy management
Euclidean distance
Euclidean geometry
Indexes
K-means
load profile
Measurement
Metering
Process controls
Shape
Shape measurement
similarity
Smart meters
Vector quantization
Online AccessGet full text

Cover

More Information
Summary:Cluster analysis is used to study the group of load profiles from smart meters to improve the operability in distribution network. The traditional K-means clustering analysis method employs Euclidean distance as similarity measurement, which is insufficient in reflecting the shape similarities of load profiles. In this letter, we propose a novel shape cluster method based on the segmented slope of load profiles. Compared with traditional K-means and two improved algorithms, the proposed method can improve the clustering accuracy and efficiency by capturing the shape features of smart metering load profiles.
ISSN:1949-3053
1949-3061
DOI:10.1109/TSG.2020.2965801