Multi-pattern data mining and recognition of primary electric appliances from single non-intrusive load monitoring data

Shengli Du, Mingchao Li, Shuai Han, Jonathan Shi, Heng Li

Research output: Journal article publicationJournal articleAcademic researchpeer-review

4 Citations (Scopus)

Abstract

The electric power industry is an essential part of the energy industry as it strengthens the monitoring and control management of household electricity for the construction of an economic power system. In this paper, a non-intrusive affinity propagation (AP) clustering algorithm is improved according to the factor graph model and the belief propagation theory. The energy data of non-intrusive monitoring consists of the actual energy consumption data of each electronic appliance. The experimental results show that this improved algorithm identifies the basic and combined class of home appliances. According to the possibility of conversion between different classes, the combination of classes is broken down into different basic classes. This method provides the basis for power management companies to allocate electricity scientifically and rationally.

Original languageEnglish
Article numberen12060992
JournalEnergies
Volume12
Issue number6
DOIs
Publication statusPublished - 1 Jan 2019

Keywords

  • AP clustering algorithm
  • data mining
  • electrical appliance
  • pattern recognition
  • power load decomposition

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Energy Engineering and Power Technology
  • Energy (miscellaneous)
  • Control and Optimization
  • Electrical and Electronic Engineering

Cite this