Matlab Application of Kohonen Self-organizing Map to Classify Consumers’ Load Profiles | Zendy

Otilia Elena Dragomir | Zendy; Florin Dragomir | Zendy; Marian Rădulescu | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Matlab Application of Kohonen Self-organizing Map to Classify Consumers’ Load Profiles

Author(s) -

Otilia Elena Dragomir,

Florin Dragomir,

Marian Rădulescu

Publication year - 2014

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2014.05.292

Subject(s) - self organizing map , computer science , matlab , data mining , artificial neural network , self organizing network , artificial intelligence , computation , pattern recognition (psychology) , object (grammar) , machine learning , algorithm , telecommunications , operating system

This paper proposes a Matlab object oriented application based on Kohonen Self- Organizing Maps (SOM) able to classify consumers’ daily load profile. Firstly, the characteristics of Kohonen self- organizing maps are briefly described in order to underline the advantages and disadvantages of these types of neural networks in classifications approaches. In the second part, data used for classification of load daily profiles is processed using statistical methods and Matlab. The result of these computations is a data base composed of daily load profiles used for SOM training. In the third part, the proposed software is tested on several scenarios in order to classify different consumers’ load profiles

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research