Self-organizing classification and identification of miscellaneous electric loads

Author

Du, L. ; He, D. ; Yang, Y. ; Restrepo, J.A. ; Lu, B. ; Harley, R.G. ; Habetler, T.G.

Author_Institution

Sch. of Electr. & Comput. Eng., Georgia Inst. of Technol., Atlanta, GA, USA

fYear

2012

fDate

22-26 July 2012

Firstpage

1

Lastpage

6

Abstract

Miscellaneous electric loads (MELs) represent a large portion of the electricity consumption. Economic and environmental impacts of energy consumption lead to needs and opportunities in energy management and saving. This paper proposes an intelligent classification and identification of MELs by extending the Self-Organizing Map (SOM) framework to a supervised manner. The SOM can classify a large amount of MELs data into several clusters by inherent similarities. The self-organizing identifier thus has the advantages of being accurate, robust, and applicable.

Keywords

energy conservation; load management; pattern classification; pattern clustering; power consumption; power engineering computing; self-organising feature maps; MEL; SOM; cluster; electricity consumption; energy consumption; energy management; energy saving; intelligent classification; intelligent identification; miscellaneous electric load; self-organizing classification; self-organizing identification; self-organizing map; Neurons; Support vector machine classification; TV; Testing; Training; Training data; Vectors; High energy efficiency buildings; Self-Organizing Map; classification; load identification;

fLanguage

English

Publisher

ieee

Conference_Titel

Power and Energy Society General Meeting, 2012 IEEE

Conference_Location

San Diego, CA

ISSN

1944-9925

Print_ISBN

978-1-4673-2727-5

Electronic_ISBN

1944-9925

Type

conf

DOI

10.1109/PESGM.2012.6343927

Filename

6343927