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USING A NEURAL NETWORK TO PREDICT ELECTRICITY GENERATION
Citation:
Capone, R. AND E.S. Kimbrough. USING A NEURAL NETWORK TO PREDICT ELECTRICITY GENERATION. U.S. Environmental Protection Agency, Washington, D.C., EPA/600/A-94/107 (NTIS PB94182276), 1994.
Description:
The paper discusses using a neural network to predict electricity generation. uch predictions are important in developing forecasts of air pollutant release and in evaluating the effectiveness of alternative policies which may reduce pollution. eural network model (NUMOD) that predicts electricity generation fueled by coal,' natural gas, and oil (whose combustion releases air pollutants) was developed to run on a personal computer. UMOD uses three linked, feed-forward neural networks, each trained with the extended delta-bar-delta paradigm. ne network predicts coal-fired generation, and its output is fed as input to the other two networks: one for gas-fired generation and the other for oil-fired generation. n addition, all three networks use inputs describing electricity demand, fuel prices, generating equipment, climate, and power pooling. earson's r, calculated at various points during training, out-of-sample tests, and performance evaluation, was greater than 0.93 and frequently greater than 0.98.
