Abstract: An adaptive training application is provided to enable an entity generating or selling electricity to predict short term market prices of this non-storable commodity in a volatile market. An artificial neural network is utilized to analyze and adapt to the generating entity's unique operational situation, plant, transmission lines, geographic location, etc. and determine all factors for which data are available and which have a relevant effect upon the market price of electricity. A training stage is provided for training the artificial neural network and determining which data are relevant and the weight of the relevant data to the ultimate determination of price. An error criterion is established to test the training of the network with respect to price forecasting. Once the network is trained it is further subject to adaptive techniques to further refine the training. The trained network input matrix is utilized in a forecasting stage to predict electricity market prices.