Intelligent electrical event recognition on general household power appliances

Author

Lei Jiang ; Suhuai Luo ; Jiaming Li

Author_Institution

Sch. of DCIT, Univ. of Newcastle, Newcastle, NSW, Australia

fYear

2014

fDate

22-25 June 2014

Firstpage

1

Lastpage

3

Abstract

The management of electricity system in home environments plays an important role in managing energy consumption and improving efficiency of energy usage. At present, nonintrusive appliance load monitoring (NIALM) techniques are the most effective approach for estimating the electrical power consumption of individual appliances. This paper presents our contributions in intelligent electrical appliance recognition in home environment. The novel method is for general power load classification and disaggregation which is mainly carried out by combining support vector machines with various power features. The experiments on real world data have demonstrated higher recognition accuracy and faster computational speed of the approach, and illustrated the effectiveness for distinguishing the different loads with promising results.

Keywords

domestic appliances; energy consumption; support vector machines; electricity system management; energy consumption management; general household power appliances; intelligent electrical event recognition; nonintrusive appliance load monitoring techniques; power load classification; support vector machines; Microwave circuits; Microwave ovens; Monitoring; Portable computers; Support vector machines; NIALM; electrical feature extraction; general power appliance; pattern recognition; smart meter;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Modeling for Power Electronics (COMPEL), 2014 IEEE 15th Workshop on

Conference_Location

Santander

Type

conf

DOI

10.1109/COMPEL.2014.6877183

Filename

6877183