With recent deregulation in electricity industry, price forecasting has become the basis for this competitive market. The precision of this forecasting is essential in bidding strategies. So far, the artificial neural networks which can find an accurate relation between the historical data and the price have been used for this purpose. One major problem is that, they usually need a large number of training data and neurons either for complex function approximation and data fitting or classification and pattern recognition. As a result, the network topology has a significant impact on the network computational time and ability to learn and also to generate unseen data from training data. To overcome these problems, a new structure using generalized neurons (GN) is adapted in this paper. The proposed structure needs a smaller data set for training. So this property of GN can be very useful for price forecasting. The data such as historical prices are not available enough for most markets. The significance, viability and efficiency of the proposed approach, in electricity price forecasting, are shown using Ontario market data points and various GN models are compared.
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