Nonintrusive load identification using extreme learning machine and TT-transform

Khairuddin Khalid, Azah Mohamed, Ramizi Mohamed, Hussain Shareef

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Citations (Scopus)

Abstract

This paper presents a new nonintrusive load identification method to disaggregate the target load in a typical commercial building. For this purpose, experiments were carried out in the laboratory implementing real load switching activity and power measurements were made with a smart meter. Nonintrusive load monitoring is performed by analysing the power signals obtained from the smart meter and detecting the operation of load appliances. A new feature extraction technique based on the time-time (TT)-transform is applied to improve the load identification. For classifying and predicting the various load operations, a new intelligent technique called as extreme learning machine with single hidden layer feedforward neural network is developed. ELM has high efficiency and simple to be implemented. The inputs to the ELM are the extracted TT-transform features together with other signals like real and reactive powers while the ELM outputs are the switching states of the load appliances. The load appliances considered in the study are the lightings, personal computers and air conditioners. The ELM accuracy is validated by testing with unknown dataset recorded by the smart meter at 1 minute sampling rate. The ELM testing results showed that the desired level of load identification can be achieved by using additional TT-transform features.

Original languageEnglish
Title of host publication2016 International Conference on Advances in Electrical, Electronic and Systems Engineering, ICAEES 2016
EditorsRosdiadee Nordin, Mohd Fais Mansor, Mahamod Ismail
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages271-276
Number of pages6
ISBN (Electronic)9781509028894
DOIs
Publication statusPublished - 2016
Event2016 International Conference on Advances in Electrical, Electronic and Systems Engineering, ICAEES 2016 - Putrajaya, Malaysia
Duration: Nov 14 2016Nov 16 2016

Publication series

Name2016 International Conference on Advances in Electrical, Electronic and Systems Engineering, ICAEES 2016

Conference

Conference2016 International Conference on Advances in Electrical, Electronic and Systems Engineering, ICAEES 2016
Country/TerritoryMalaysia
CityPutrajaya
Period11/14/1611/16/16

Keywords

  • Extreme learning machine
  • Feature extraction
  • Nonintrusive load monitoring
  • TT-transform

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Biomedical Engineering
  • Control and Systems Engineering
  • Hardware and Architecture
  • Computer Networks and Communications
  • Instrumentation

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