Electrical Power Prediction through a Combination of Multilayer Perceptron with Water Cycle Ant Lion and Satin Bowerbird Searching Optimizers

Hossein Moayedi and Amir Mosavi
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Hossein Moayedi: Informetrics Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam
Amir Mosavi: School of Economics and Business, Norwegian University of Life Sciences, 1430 Ås, Norway

Sustainability, 2021, vol. 13, issue 4, 1-18

Abstract: Predicting the electrical power (P E ) 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 E , 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 E 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 E and influential factors with less than 1% error. Likewise, examining the test results revealed that this model can forecast the P E 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.

Keywords: power plant; electrical power modeling; metaheuristic optimization; water cycle algorithm; machine learning; deep learning; big data; energy; deep learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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