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Title: System and method employing a self-organizing map load feature database to identify electric load types of different electric loads

Abstract

A method identifies electric load types of a plurality of different electric loads. The method includes providing a self-organizing map load feature database of a plurality of different electric load types and a plurality of neurons, each of the load types corresponding to a number of the neurons; employing a weight vector for each of the neurons; sensing a voltage signal and a current signal for each of the loads; determining a load feature vector including at least four different load features from the sensed voltage signal and the sensed current signal for a corresponding one of the loads; and identifying by a processor one of the load types by relating the load feature vector to the neurons of the database by identifying the weight vector of one of the neurons corresponding to the one of the load types that is a minimal distance to the load feature vector.

Inventors:
; ; ; ; ; ;
Publication Date:
Research Org.:
Eaton Corporation Cleveland, OH (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1134210
Patent Number(s):
8,756,181
Application Number:
13/304,758
Assignee:
Eaton Corporation (Cleveland, OH); Georgia Tech Research Corporation (Atlanta, GA) GFO
DOE Contract Number:  
EE0003911
Resource Type:
Patent
Resource Relation:
Patent File Date: 2011 Nov 28
Country of Publication:
United States
Language:
English
Subject:
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION

Citation Formats

Lu, Bin, Harley, Ronald G., Du, Liang, Yang, Yi, Sharma, Santosh K., Zambare, Prachi, and Madane, Mayura A. System and method employing a self-organizing map load feature database to identify electric load types of different electric loads. United States: N. p., 2014. Web.
Lu, Bin, Harley, Ronald G., Du, Liang, Yang, Yi, Sharma, Santosh K., Zambare, Prachi, & Madane, Mayura A. System and method employing a self-organizing map load feature database to identify electric load types of different electric loads. United States.
Lu, Bin, Harley, Ronald G., Du, Liang, Yang, Yi, Sharma, Santosh K., Zambare, Prachi, and Madane, Mayura A. Tue . "System and method employing a self-organizing map load feature database to identify electric load types of different electric loads". United States. doi:. https://www.osti.gov/servlets/purl/1134210.
@article{osti_1134210,
title = {System and method employing a self-organizing map load feature database to identify electric load types of different electric loads},
author = {Lu, Bin and Harley, Ronald G. and Du, Liang and Yang, Yi and Sharma, Santosh K. and Zambare, Prachi and Madane, Mayura A.},
abstractNote = {A method identifies electric load types of a plurality of different electric loads. The method includes providing a self-organizing map load feature database of a plurality of different electric load types and a plurality of neurons, each of the load types corresponding to a number of the neurons; employing a weight vector for each of the neurons; sensing a voltage signal and a current signal for each of the loads; determining a load feature vector including at least four different load features from the sensed voltage signal and the sensed current signal for a corresponding one of the loads; and identifying by a processor one of the load types by relating the load feature vector to the neurons of the database by identifying the weight vector of one of the neurons corresponding to the one of the load types that is a minimal distance to the load feature vector.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Tue Jun 17 00:00:00 EDT 2014},
month = {Tue Jun 17 00:00:00 EDT 2014}
}

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Works referenced in this record:

Artificial neural networks in measurements
journal, March 1998


Nonintrusive load disaggregation computer program to estimate the energy consumption of major end uses in residential buildings
journal, September 2000