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 ;
|
Holdings |
Library |
Call Number |
Additional Info |
Location |
Last Modified |
Checkout Status |
NTIS |
PB94-182276 |
Some EPA libraries have a fiche copy filed under the call number shown. |
|
07/26/2022 |
|
Collation |
8p |
Abstract |
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. |
Supplementary Notes |
Presented at the World Congress on Neural Networks, San Diego, CA., June 5-9, 1994. Sponsored by Environmental Protection Agency, Research Triangle Park, NC. Air and Energy Engineering Research Lab. |
NTIS Title Notes |
Rept. for Dec 93-May 94. |
Title Annotations |
Reprint: Using a Neural Network to Predict Electricity Generation. |
Category Codes |
97I; 97R; 81A; 68A |
NTIS Prices |
PC A02/MF A01 |
Primary Description |
600/13 |
Document Type |
NT |
Cataloging Source |
NTIS/MT |
Control Number |
421724744 |
Origin |
NTIS |
Type |
CAT |