Title :
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
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;
Conference_Titel :
Power and Energy Society General Meeting, 2012 IEEE
Conference_Location :
San Diego, CA
Print_ISBN :
978-1-4673-2727-5
Electronic_ISBN :
1944-9925
DOI :
10.1109/PESGM.2012.6343927
