A Novel Artificial Neural Network Topology to Enhance Combined Cycle Power Plants Modeling Capabilities
DOI:
https://doi.org/10.37965/jait.2025.0710Keywords:
artificial neural network, combined cycle plant, Levenberg–Marquardt, regression analysis, thermal plants powerAbstract
Neural networks are prominent among concurrent advanced technology techniques due to their capacity to deal with massive long-term datasets and the nonlinear modeling in combined cycle power plants. This paper studies an adaptation of the Levenberg–Marquardt training algorithm to train and assess a combined cycle power plant output. The most robust electric power predictions are identified for 70% of the training data, 15% of the validation, and 15% of the testing data. The sensitivity impact of reducing the design variables to two and three via a modern methodology for the electric power estimation at a reduced cost constraint is presented. The adapted actual experimental data (9568) from six years of four input parameters, ambient temperature, exhaust vacuum, ambient pressure, and relative humidity, were used. These input parameters were combined in different datasets as the ambient temperature (P1), the exhaust vacuum (P2), and the ambient pressure (P3), applied to different settings using (P1+P2, P1+P3, P2+P3) for the two input variables and (P1+P2+P3) for the three variables. The implementation of the Levenberg–Marquardt training code for a hidden layer size of (20, 500) contributes to the output power prediction. The regression values obtained for the two variables combined datasets (P1+P2, P1+P3, and P2+P3) were 0.9701, 0.9658, and 0.9401, thus highlighting the superiority of the (P1+P2) dataset. When the design variables were increased to three (P1+P2+P3), a better prediction of electric power in terms of the improved regression value 0.971 was observed with a mean square error of 13.8389. These mean square error and regression coefficient values for both network settings (P1+P2) and (P1+P2+P3) showed improved performance compared to past studies. Hence, this new approach of neural network configuration with the three input parameters provides a novel prediction of the output power, which can be used to validate the combined cycle power plants and has computational benefits in other real-world applications.
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