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Next-day electricity-price forecasting using a hybrid network

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3 Author(s)
Fan, S. ; Osaka Sangyo Univ., Daito Osaka ; Mao, C. ; Chen, L.

The paper proposes a novel method of forecasting short-term electricity price based on a two-stage hybrid network of self-organised map (SOM) and support-vector machine (SVM). In the first stage, a SOM network is applied to cluster the input-data set into several subsets in an unsupervised manner. Then, a group of SVMs is used to fit the training data of each subset in the second stage in a supervised way. With the trained network, one can predict straightforwardly the next-day hourly electricity prices. To confirm its effectiveness, the proposed model has been trained and tested on the data of historical energy prices from the New England electricity market.

Published in:

Generation, Transmission & Distribution, IET  (Volume:1 ,  Issue: 1 )

Date of Publication:

January 2007

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