A SELF ORGANIZING MAP BASED OF ELECTRIC LOAD CLASSIFICATION

Mahdi Farhadi

DOI Number
10.2298/FUEE1804571F
First page
571
Last page
583

Abstract


It is of vital importance to use proper training data to perform accurate short-term 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.


Keywords

Short-term load forecasting, similar sampling process, Kohonen self-organizing map, pattern recognition, electric load classification, load classifier

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