Electrical power output prediction of combined cycle power plants using a recurrent neural network optimized by waterwheel plant algorithm

Abstract

It is difficult to analyze and anticipate the power output of Combined Cycle Power Plants (CCPPs) when considering operational thermal variables such as ambient pressure, vacuum, relative humidity, and temperature. Our data visualization study shows strong non-linearity in the experimental data. We observe that CCPP energy production increases linearly with temperature but not pressure. We offer the Waterwheel Plant Algorithm (WWPA), a unique metaheuristic optimization method, to fine-tune Recurrent Neural Network hyperparameters to improve prediction accuracy. A robust mathematical model for energy production prediction is built and validated using anticipated and experimental data residuals. The residuals’ uniformity above and below the regression line suggests acceptable prediction errors. Our mathematical model has an R-squared value of 0.935 and 0.999 during training and testing, demonstrating its outstanding predictive accuracy. This research provides an accurate way to forecast CCPP energy output, which could improve operational efficiency and resource utilization in these power plants.