Application of the Kohonen network to short-term load forecasting

This paper analyses the application of Kohonen's self-organizing feature map to short-term forecasting of daily electrical load. The aim of the paper is to study the feasibility of the Kohonen's self-organizing feature maps for the classification of electrical loads. The network not only `learns' similarities of load patterns in a unsupervised manner, but it uses the information stored in the weight vectors of the Kohonen network to forecast the future load. The results are evaluated by using several months of hourly load data of a real system to train the network, and forecasting the daily loads for two periods of one month. The method is then improved by adding a second type of neural network for weather sensitive correction of the load previously calculated with the Kohonen network. This second type of network is a one-layered linear delta rule network

Published in:
ANNPS '93. Proceedings of the Second International Forum on Applications of Neural Networks to Power Systems (Cat. No.93TH0532-2), 407 - 12
power systems;power engineering computing;training;Kohonen network;short-term load forecasting;application;self-organizing feature map;classification;weight vectors;weather sensitive correction;one-layered linear delta rule network;

 Record created 2007-04-04, last modified 2018-01-27

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