Modelling and Output Power Estimation of a Combined Gas Plant and a Combined Cycle Plant Using an Artificial Neural Network Approach
Artificial neural networks (ANNs) have gained prominence among contemporary computing techniques due to their capacity to handle complicated stochastic datasets and nonlinear modelling in combined gas and combined cycle power (COGAS) plants. Researchers, academicians, and stakeholders have been unab...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-01-01
|
| Series: | Journal of Engineering |
| Online Access: | http://dx.doi.org/10.1155/2024/5540010 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849405127596703744 |
|---|---|
| author | Vasileios Xezonakis Olusegun David Samuel Christopher Chintua Enweremadu |
| author_facet | Vasileios Xezonakis Olusegun David Samuel Christopher Chintua Enweremadu |
| author_sort | Vasileios Xezonakis |
| collection | DOAJ |
| description | Artificial neural networks (ANNs) have gained prominence among contemporary computing techniques due to their capacity to handle complicated stochastic datasets and nonlinear modelling in combined gas and combined cycle power (COGAS) plants. Researchers, academicians, and stakeholders have been unable to predict, ensure effective operation, and prevent power outages in COGAS due to the nonlinearity. The first implementation of the simultaneous adoption of three types of ANNs using Levenberg–Marquardt (LM), Bayesian regularisation (BR), and scaled conjugate gradient (SCG) configurations for training and assessing a combined cycle power plant output is presented. The dataset used in this research is a 9568-unit full combined cycle power plant basis load dataset, accessible through the public UCI Machine Learning Repository. It incorporates ambient temperature, exhaust vacuum, ambient pressure, and relative humidity as input parameters to predict the electric output power. The most accurate and dependable electric power predictions could be identified for 70% of the total data, of which 6698 were trained, 15% were tested, and 15% were validated (2870). By using the three training techniques, namely, LM, BR, and SCG, the parameterized networks are studied, increasing the number of hidden layers from 20 to 500. The lowest root-mean-square error value for a multilayer perceptron (MLP) architecture is 3.631%, which is lower than the values of 4.17%, 4.35%, and 4.63% for comparable MLP structures (20 to 500), documented in the literature. The LM and BR algorithms outperform SCG. These adopted algorithms could be a cutting-edge application in the power plant industry and other real-world applications for reliable solutions, to satisfy emerging societal needs with environmental benefits. |
| format | Article |
| id | doaj-art-4707c4ea4c704a3a85eda121a50d0a71 |
| institution | Kabale University |
| issn | 2314-4912 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Engineering |
| spelling | doaj-art-4707c4ea4c704a3a85eda121a50d0a712025-08-20T03:36:45ZengWileyJournal of Engineering2314-49122024-01-01202410.1155/2024/5540010Modelling and Output Power Estimation of a Combined Gas Plant and a Combined Cycle Plant Using an Artificial Neural Network ApproachVasileios Xezonakis0Olusegun David Samuel1Christopher Chintua Enweremadu2Department of Mechanical EngineeringDepartment of Mechanical EngineeringDepartment of Mechanical EngineeringArtificial neural networks (ANNs) have gained prominence among contemporary computing techniques due to their capacity to handle complicated stochastic datasets and nonlinear modelling in combined gas and combined cycle power (COGAS) plants. Researchers, academicians, and stakeholders have been unable to predict, ensure effective operation, and prevent power outages in COGAS due to the nonlinearity. The first implementation of the simultaneous adoption of three types of ANNs using Levenberg–Marquardt (LM), Bayesian regularisation (BR), and scaled conjugate gradient (SCG) configurations for training and assessing a combined cycle power plant output is presented. The dataset used in this research is a 9568-unit full combined cycle power plant basis load dataset, accessible through the public UCI Machine Learning Repository. It incorporates ambient temperature, exhaust vacuum, ambient pressure, and relative humidity as input parameters to predict the electric output power. The most accurate and dependable electric power predictions could be identified for 70% of the total data, of which 6698 were trained, 15% were tested, and 15% were validated (2870). By using the three training techniques, namely, LM, BR, and SCG, the parameterized networks are studied, increasing the number of hidden layers from 20 to 500. The lowest root-mean-square error value for a multilayer perceptron (MLP) architecture is 3.631%, which is lower than the values of 4.17%, 4.35%, and 4.63% for comparable MLP structures (20 to 500), documented in the literature. The LM and BR algorithms outperform SCG. These adopted algorithms could be a cutting-edge application in the power plant industry and other real-world applications for reliable solutions, to satisfy emerging societal needs with environmental benefits.http://dx.doi.org/10.1155/2024/5540010 |
| spellingShingle | Vasileios Xezonakis Olusegun David Samuel Christopher Chintua Enweremadu Modelling and Output Power Estimation of a Combined Gas Plant and a Combined Cycle Plant Using an Artificial Neural Network Approach Journal of Engineering |
| title | Modelling and Output Power Estimation of a Combined Gas Plant and a Combined Cycle Plant Using an Artificial Neural Network Approach |
| title_full | Modelling and Output Power Estimation of a Combined Gas Plant and a Combined Cycle Plant Using an Artificial Neural Network Approach |
| title_fullStr | Modelling and Output Power Estimation of a Combined Gas Plant and a Combined Cycle Plant Using an Artificial Neural Network Approach |
| title_full_unstemmed | Modelling and Output Power Estimation of a Combined Gas Plant and a Combined Cycle Plant Using an Artificial Neural Network Approach |
| title_short | Modelling and Output Power Estimation of a Combined Gas Plant and a Combined Cycle Plant Using an Artificial Neural Network Approach |
| title_sort | modelling and output power estimation of a combined gas plant and a combined cycle plant using an artificial neural network approach |
| url | http://dx.doi.org/10.1155/2024/5540010 |
| work_keys_str_mv | AT vasileiosxezonakis modellingandoutputpowerestimationofacombinedgasplantandacombinedcycleplantusinganartificialneuralnetworkapproach AT olusegundavidsamuel modellingandoutputpowerestimationofacombinedgasplantandacombinedcycleplantusinganartificialneuralnetworkapproach AT christopherchintuaenweremadu modellingandoutputpowerestimationofacombinedgasplantandacombinedcycleplantusinganartificialneuralnetworkapproach |