Improved butterfly optimization algorithm applied to prediction of combined cycle power plant

Wang, Xiao; Sun, Xiao-Xue; Chu, Shu-Chuan; Watada, Junzo; Pan, Jeng‐Shyang

doi:10.1016/j.matcom.2022.08.009

Cited by 4 publications

(2 citation statements)

References 37 publications

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“…Wang et al [51] have developed a hybrid optimisation method between the butterfy optimisation algorithm and the support vector regression model. Tis was used to estimate the electric power accurately, thereby avoiding local optimisation outcomes.…”

Section: Literature Reviewmentioning

confidence: 99%

Modelling and Output Power Estimation of a Combined Gas Plant and a Combined Cycle Plant Using an Artificial Neural Network Approach

Xezonakis,

Samuel,

Enweremadu

2024

Journal of Engineering

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Artificial neural networks (ANNs) have gained prominence among contemporary computing techniques due to their capacity to handle complicated stochastic datasets and nonlinear modelling in combined gas and combined cycle power (COGAS) plants. Researchers, academicians, and stakeholders have been unable to predict, ensure effective operation, and prevent power outages in COGAS due to the nonlinearity. The first implementation of the simultaneous adoption of three types of ANNs using Levenberg–Marquardt (LM), Bayesian regularisation (BR), and scaled conjugate gradient (SCG) configurations for training and assessing a combined cycle power plant output is presented. The dataset used in this research is a 9568-unit full combined cycle power plant basis load dataset, accessible through the public UCI Machine Learning Repository. It incorporates ambient temperature, exhaust vacuum, ambient pressure, and relative humidity as input parameters to predict the electric output power. The most accurate and dependable electric power predictions could be identified for 70% of the total data, of which 6698 were trained, 15% were tested, and 15% were validated (2870). By using the three training techniques, namely, LM, BR, and SCG, the parameterized networks are studied, increasing the number of hidden layers from 20 to 500. The lowest root-mean-square error value for a multilayer perceptron (MLP) architecture is 3.631%, which is lower than the values of 4.17%, 4.35%, and 4.63% for comparable MLP structures (20 to 500), documented in the literature. The LM and BR algorithms outperform SCG. These adopted algorithms could be a cutting-edge application in the power plant industry and other real-world applications for reliable solutions, to satisfy emerging societal needs with environmental benefits.

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Section: Literature Reviewmentioning

confidence: 99%

Modelling and Output Power Estimation of a Combined Gas Plant and a Combined Cycle Plant Using an Artificial Neural Network Approach

Xezonakis,

Samuel,

Enweremadu

2024

Journal of Engineering

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“…Butterfly optimization algorithm (BOA) was proposed by Arora and Singh in 2019, 38 which was inspired by the foraging behavior of butterflies. Since BOA has the advantages of few parameters and easy implementation, it has been widely applied in building, 39 medicine 40 electricity 41 and other fields. However, these applications all used BOA to solve continuous problems.…”

Section: Introductionmentioning

confidence: 99%