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Comparative study of application of different supervised learning methods in forecasting future states of NPPs operating parameters

Moshkbar Bakhshayesh, K ; Sharif University of Technology | 2019

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  1. Type of Document: Article
  2. DOI: 10.1016/j.anucene.2019.04.031
  3. Publisher: Elsevier Ltd , 2019
  4. Abstract:
  5. In this paper, some important operating parameters of nuclear power plants (NPPs) transients are forecasted using different supervised learning methods including feed-forward back propagation (FFBP) neural networks such as cascade feed-forward neural network (CFFNN), statistical methods such as support vector regression (SVR), and localized networks such as radial basis network (RBN). Different learning algorithms, including gradient descent (GD), gradient descent with momentum (GDM), scaled conjugate gradient (SCG), Levenberg-Marquardt (LM), and Bayesian regularization (BR) are used in CFFNN method. SVR method is used with different kernel functions including Gaussian, polynomial, and linear. RBN is used with radial activation function. Comparison of the results indicates that learning algorithms based on Gaussian distribution function (i.e. BR algorithm) and Gaussian kernel/activation functions are, in general, more precise for time series prediction. Moreover, learning methods based on Gaussian function lead to acceptable results in prediction of complicated time series, such as core inlet flowrate of large break loss of coolant accident (LBLOCA) which are changed irregularly and drastically. In other words, Gaussian learning algorithms/kernel functions/activation functions are appropriate choices for NPPs parameters forecasting. © 2019 Elsevier Ltd
  6. Keywords:
  7. Bayesian regularization ; Cascade feed-forward neural network ; Gaussian function ; Localized networks ; NPP parameters forecasting ; Support vector regression ; Backpropagation ; Bayesian networks ; Conjugate gradient method ; Distribution functions ; Forecasting ; Gaussian distribution ; Loss of coolant accidents ; Machine learning ; Nuclear fuels ; Nuclear power plants ; Reactor cores ; Time series ; Feed-forward back propagation ; Gaussian functions ; Large break loss of coolant accidents ; Scaled conjugate gradients ; Supervised learning methods ; Support vector regression (SVR) ; Time series prediction ; Learning algorithms
  8. Source: Annals of Nuclear Energy ; Volume 132 , 2019 , Pages 87-99 ; 03064549 (ISSN)
  9. URL: https://www.sciencedirect.com/science/article/pii/S0306454919302154