Residential load signature analysis for their segregation using wavelet—SVM

Munendra Singh, Sanjeev Kumar, Sunil Semwal, R. S. Prasad

Research output: Contribution to journalArticlepeer-review

19 Citations (Scopus)


The unique power consumption pattern of each appliance or a combination of appliances can be analyzed using their load signatures which can be acquired from a single point. It is quite difficult to disaggregate the similar kind of home appliances because of their similar characteristics. Wavelet coefficients of load signature have been chosen as the feature vectors which reflected the edge over other features. These coefficients serve as input data for the classifier. By considering various classification algorithms a comparison has been made and the best algorithm was investigated which is the linear Support Vector Machine (SVM) for the selected similar appliances. The results of laboratory experiment promise a new application for smart meters.

Original languageEnglish
Pages (from-to)863-871
Number of pages9
JournalLecture Notes in Electrical Engineering
Publication statusPublished - 2015

All Science Journal Classification (ASJC) codes

  • Industrial and Manufacturing Engineering


Dive into the research topics of 'Residential load signature analysis for their segregation using wavelet—SVM'. Together they form a unique fingerprint.

Cite this