Pattern recognition as a tool to support decision making in the management of the electric sector. Part II: A new method based on clustering of multivariate time series

•Method new of clustering and pattern recognition of multivariate time series.•Proposes a approach to recognize consumption patterns in the electricity sector.•Unlike Fuzzy C-Means method, the number of clusters is obtained automatically.•The results demonstrate its good performance in samples with...

Full description

Saved in:
Bibliographic Details
Published in:International journal of electrical power & energy systems 2015-05, Vol.67, p.613-626
Main Authors: Ferreira, Adonias Magdiel Silva, de Oliveira Fontes, Cristiano Hora, Cavalcante, Carlos Arthur Mattos Teixeira, Marambio, Jorge Eduardo Soto
Format: Article
Language:English
Subjects:
Algorithms
Brazil
Clustering
Decision making
Electricity distribution
Fuzzy
Multivariate time series
Pattern
Pattern recognition
Refrigerators
Time series
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:•Method new of clustering and pattern recognition of multivariate time series.•Proposes a approach to recognize consumption patterns in the electricity sector.•Unlike Fuzzy C-Means method, the number of clusters is obtained automatically.•The results demonstrate its good performance in samples with heterogeneous data. This work presents a new method for the clustering and pattern recognition of multivariate time series (CPT-M) based on multivariate statistics. The algorithm comprises four steps that extract essential features of multivariate time series of residential users with emphasis on seasonal and temporal profile, among others. The method was successfully implemented and tested in the context of an energy efficiency program carried out by the Electric Company of Alagoas (Brazil) that considers, among others, the analysis of the impact of replacing refrigerators in low-income consumers’ homes in several towns located within the state of Alagoas (Brazil). The results were compared with a well-known method of time series clustering already established in the literature, the Fuzzy C-Means (FCM). Unlike C-means models of clustering, the CPT-M method is also capable to obtain directly the number of clusters. The analysis confirmed that the CPT-M method was capable to identify a greater diversity of patterns, showing the potential of this method in better recognition of consumption patterns considering simultaneously the effect of other variables in additional to load curves. This represents an important aspect to the process of decision making in the energy distribution sector.
ISSN:0142-0615
1879-3517
DOI:10.1016/j.ijepes.2014.12.001