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

Author

Listed:
  • Hossein Moayedi

    (Informetrics Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam
    Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam)

  • Amir Mosavi

    (School of Economics and Business, Norwegian University of Life Sciences, 1430 Ås, Norway
    School of the Built Environment, Oxford Brookes University, Oxford OX3 0BP, UK
    John von Neumann Faculty of Informatics, Obuda University, 1034 Budapest, Hungary
    Department of Informatics, Selye Janos University, 94501 Komarom, Slovakia)

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.

Suggested Citation

  • Hossein Moayedi & Amir Mosavi, 2021. "Electrical Power Prediction through a Combination of Multilayer Perceptron with Water Cycle Ant Lion and Satin Bowerbird Searching Optimizers," Sustainability, MDPI, vol. 13(4), pages 1-18, February.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:4:p:2336-:d:503295
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