Combined cycle power plant with indirect dry cooling tower forecasting using artificial neural network

Combined cycle power plant with indirect dry cooling tower forecasting using artificial neural network

Application of Artificial Neural Network (ANN) in modeling of combined cycle power plant (CCPP) with dry cooling tower (Heller tower) has been investigated in this paper. Prediction of power plant output (megawatt) under different working conditions was made using multi-layer feed-forward ANN and tr...

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Main Author: Asad Dehghani Samani
Format: Article
Language:English
Published: Growing Science 2017-07-01
Series:Decision Science Letters
Subjects:
ANN
CCPP
Regression
ST
Dry cooling tower
Megawatt
Forecasting
Online Access:http://www.growingscience.com/dsl/Vol7/dsl_2017_23.pdf
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spelling doaj-e889e8d8357948d58b755d09fdc1a6d92020-11-24T23:48:03ZengGrowing ScienceDecision Science Letters1929-58041929-58122017-07-017213114210.5267/j.dsl.2017.6.004Combined cycle power plant with indirect dry cooling tower forecasting using artificial neural network Asad Dehghani SamaniApplication of Artificial Neural Network (ANN) in modeling of combined cycle power plant (CCPP) with dry cooling tower (Heller tower) has been investigated in this paper. Prediction of power plant output (megawatt) under different working conditions was made using multi-layer feed-forward ANN and training was performed with operational data using back-propagation. Two ANN network was constructed for the steam turbine (ST) and the main cooling system(MCS). Results indicate that the ANN model is effective in predicting the power plant output with good accuracy.http://www.growingscience.com/dsl/Vol7/dsl_2017_23.pdfANNCCPPRegressionSTDry cooling towerMegawattForecasting
collection DOAJ
language English
format Article
sources DOAJ
author Asad Dehghani Samani
spellingShingle Asad Dehghani Samani
Combined cycle power plant with indirect dry cooling tower forecasting using artificial neural network
Decision Science Letters
ANN
CCPP
Regression
ST
Dry cooling tower
Megawatt
Forecasting
author_facet Asad Dehghani Samani
author_sort Asad Dehghani Samani
title Combined cycle power plant with indirect dry cooling tower forecasting using artificial neural network
title_short Combined cycle power plant with indirect dry cooling tower forecasting using artificial neural network
title_full Combined cycle power plant with indirect dry cooling tower forecasting using artificial neural network
title_fullStr Combined cycle power plant with indirect dry cooling tower forecasting using artificial neural network
title_full_unstemmed Combined cycle power plant with indirect dry cooling tower forecasting using artificial neural network
title_sort combined cycle power plant with indirect dry cooling tower forecasting using artificial neural network
publisher Growing Science
series Decision Science Letters
issn 1929-5804
1929-5812
publishDate 2017-07-01
description Application of Artificial Neural Network (ANN) in modeling of combined cycle power plant (CCPP) with dry cooling tower (Heller tower) has been investigated in this paper. Prediction of power plant output (megawatt) under different working conditions was made using multi-layer feed-forward ANN and training was performed with operational data using back-propagation. Two ANN network was constructed for the steam turbine (ST) and the main cooling system(MCS). Results indicate that the ANN model is effective in predicting the power plant output with good accuracy.
topic ANN
CCPP
Regression
ST
Dry cooling tower
Megawatt
Forecasting
url http://www.growingscience.com/dsl/Vol7/dsl_2017_23.pdf
work_keys_str_mv AT asaddehghanisamani combinedcyclepowerplantwithindirectdrycoolingtowerforecastingusingartificialneuralnetwork
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