Open AccessA self organizing map (SOM) based electric load classification
Author(s) -
Mahdi Farhadi
Publication year - 2018
Publication title -
facta universitatis. series electronics and energetics/facta universitatis. series: electronics and energetics
Language(s) - English
Resource type - Journals
eISSN - 2217-5997
pISSN - 0353-3670
DOI - 10.2298/fuee1804571f
Subject(s) - self organizing map , artificial neural network , electrical load , computer science , artificial intelligence , classifier (uml) , pattern recognition (psychology) , grid , data mining , machine learning , engineering , voltage , mathematics , geometry , electrical engineering
It is of vital importance to use proper training data to perform accurate shortterm load forecasting (STLF) based on artificial neural networks. The pattern of the loads which are used for the training of Kohonen Self Organizing Map (SOM) neural network in STLF models should be of the highest similarity with the pattern of the electric load of the forecasting day. In this paper, an electric load classifier model is proposed which relies on the pattern recognition capability of SOM. The performance of the proposed electric load classifier method is evaluated by Iran electric grid data. The proposed method requires a very few number of training samples for training the Kohonen neural network of the STLF model and can accurately predict electric load in the network.