Record Display for the EPA National Library Catalog


OLS Field Name OLS Field Data
Main Title Using a Neural Network to Predict Electricity Generation.
Author Capone, R. L. ; Kimbrough, E. S. ;
CORP Author Capone (Ronald L.) and Associates, Arlington, VA.;Environmental Protection Agency, Research Triangle Park, NC. Air and Energy Engineering Research Lab.
Publisher May 94
Year Published 1994
Report Number EPA-68-D1-0146; EPA/600/A-94/107;
Stock Number PB94-182276
Additional Subjects Electric power ; Power generation ; Neural networks ; Fossil fuels ; Natural gas ; Petroleum ; Coal ; Combustion products ; Air pollution detection ; Forecasting ; Power plants ; Computerized simulation ; Mathematical models ; Reprints ;
Library Call Number Additional Info Location Last
NTIS  PB94-182276 Most EPA libraries have a fiche copy filed under the call number shown. Check with individual libraries about paper copy. NTIS 09/01/1994
Collation 8p
Predicting electricity generation is important to developing forecasts of air pollutant release and to evaluating the effectiveness of alternative policies which may reduce pollution. A neural network model (NUMOD) predicting electricity generation fueled by coal, natural gas, and oil (whose combustion released air pollutants) was developed to run on a personal computer. NUMOD uses three linked, feed-forward neural networks, each trained with the extended delta-bar-delta paradigm. One network predicts coal-fired generation. Its output is fed as input to each of the other two networks: one for gas-fired generation and the other for oil-fired generation. In addition, all three networks use inputs describing electricity demand, fuel prices, generating equipment, climate, and power pooling. Pearson'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.