Skip to Main Content
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.