Nitin Singh
ABSTRACT
Price forecasting has become one of the key factors for competition in the deregulated electricity market and it is necessary to know future electricity prices for generating companies as their profitability depends on them. The precision of the price forecasting model is essential in bidding strategies. The major problem with the models based on artificial neural networks is that they usually need a large number of training data and neurons. To overcome these issues, a new structure using generalized neurons (GN) is being adopted which require smaller data set for training which makes it very useful for price forecasting for places where historical data available is not sufficient to use ANN. In the proposed work, a hybrid model using generalized neuron is used for comparing the performance using different cost functions.
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Index Terms
- A Modified Cost Function Generalized Neuron for Electricity Price Forecasting in Deregulated Power Markets
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