摘要：Predicting the electrical power (P<sub>E</sub>) output is a significant step toward the sustainable development of combined cycle power plants. Due to the effect of several parameters on the simulation of P<sub>E</sub>, utilizing a robust method is of high importance. Hence, in this study, a potent metaheuristic strategy, namely, the water cycle algorithm (WCA), is employed to solve this issue. First, a nonlinear neural network framework is formed to link the P<sub>E</sub> with influential parameters. Then, the network is optimized by the WCA algorithm. A publicly available dataset is used to feed the hybrid model. Since the WCA is a population-based technique, its sensitivity to the population size is assessed by a trial-and-error effort to attain the most suitable configuration. The results in the training phase showed that the proposed WCA can find an optimal solution for capturing the relationship between the P<sub>E</sub> and influential factors with less than 1% error. Likewise, examining the test results revealed that this model can forecast the P<sub>E</sub> with high accuracy. Moreover, a comparison with two powerful benchmark techniques, namely, ant lion optimization and a satin bowerbird optimizer, pointed to the WCA as a more accurate technique for the sustainable design of the intended system. Lastly, two potential predictive formulas, based on the most efficient WCAs, are extracted and presented.
